{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup\n",
    "import time\n",
    "import os\n",
    "\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load house records into dataframe\n",
    "data_column = ['Car_No', 'Car_Price',  'Car_ORC', 'Car_Title', 'Car_Year', 'Car_Brand', 'Car_Model','Car_Location','Car_Mileage(km)',  'Car_Type', 'Car_Gear', 'Car_Seat', 'Car_Fuel(cc)',  'Car_Door_Num',  'Car_Wof_Exp', 'Car_Fuel_Type', 'Car_Reg', 'Car_Color']\n",
    "Car_data_target = pd.read_csv('../datasets/car_result_FINAL.txt', sep = '|', dtype = 'str', header=None)\n",
    "Car_data_target.columns = data_column \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13442, 18)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check on duplicate values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Wof_Exp</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Reg</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>89038</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2007 Lexus IS250</td>\n",
       "      <td>2007</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>IS250</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107000</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>85695</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2011 Volkswagen Golf 1.4 90Kw</td>\n",
       "      <td>2011</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>32800</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>May 2021</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5533</th>\n",
       "      <td>75514</td>\n",
       "      <td>8995</td>\n",
       "      <td>0</td>\n",
       "      <td>2004 Nissan Murano Full Leather Nice Car</td>\n",
       "      <td>2004</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Murano</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>138452</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>3498</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Car_No Car_Price Car_ORC                                 Car_Title  \\\n",
       "594   89038      9990       0                          2007 Lexus IS250   \n",
       "1342  85695      9990       0             2011 Volkswagen Golf 1.4 90Kw   \n",
       "5533  75514      8995       0  2004 Nissan Murano Full Leather Nice Car   \n",
       "\n",
       "     Car_Year   Car_Brand Car_Model Car_Location Car_Mileage(km)   Car_Type  \\\n",
       "594      2007       Lexus     IS250     Auckland          107000      Sedan   \n",
       "1342     2011  Volkswagen      Golf     Auckland           32800  Hatchback   \n",
       "5533     2004      Nissan    Murano     Auckland          138452     RV/SUV   \n",
       "\n",
       "       Car_Gear Car_Seat Car_Fuel(cc) Car_Door_Num Car_Wof_Exp Car_Fuel_Type  \\\n",
       "594   Tiptronic      NaN         2500          NaN         NaN        Petrol   \n",
       "1342  Automatic        5         1389          NaN         NaN        Petrol   \n",
       "5533  Automatic        5         3498            5         NaN        Petrol   \n",
       "\n",
       "       Car_Reg Car_Color  \n",
       "594        NaN     White  \n",
       "1342  May 2021       Red  \n",
       "5533       NaN      Gold  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[(Car_data_target.duplicated(['Car_No']) == True)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Wof_Exp</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Reg</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>89038</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2007 Lexus IS250</td>\n",
       "      <td>2007</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>IS250</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107000</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apr 2022</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>89038</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2007 Lexus IS250</td>\n",
       "      <td>2007</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>IS250</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107000</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>85695</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2011 Volkswagen Golf 1.4 90Kw</td>\n",
       "      <td>2011</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>32800</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mar 2022</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>May 2021</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>85695</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2011 Volkswagen Golf 1.4 90Kw</td>\n",
       "      <td>2011</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>32800</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>May 2021</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5532</th>\n",
       "      <td>75514</td>\n",
       "      <td>8995</td>\n",
       "      <td>0</td>\n",
       "      <td>2004 Nissan Murano Full Leather Nice Car</td>\n",
       "      <td>2004</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Murano</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>138452</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>3498</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5533</th>\n",
       "      <td>75514</td>\n",
       "      <td>8995</td>\n",
       "      <td>0</td>\n",
       "      <td>2004 Nissan Murano Full Leather Nice Car</td>\n",
       "      <td>2004</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Murano</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>138452</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>3498</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Car_No Car_Price Car_ORC                                 Car_Title  \\\n",
       "593   89038      9990       0                          2007 Lexus IS250   \n",
       "594   89038      9990       0                          2007 Lexus IS250   \n",
       "1341  85695      9990       0             2011 Volkswagen Golf 1.4 90Kw   \n",
       "1342  85695      9990       0             2011 Volkswagen Golf 1.4 90Kw   \n",
       "5532  75514      8995       0  2004 Nissan Murano Full Leather Nice Car   \n",
       "5533  75514      8995       0  2004 Nissan Murano Full Leather Nice Car   \n",
       "\n",
       "     Car_Year   Car_Brand Car_Model Car_Location Car_Mileage(km)   Car_Type  \\\n",
       "593      2007       Lexus     IS250     Auckland          107000      Sedan   \n",
       "594      2007       Lexus     IS250     Auckland          107000      Sedan   \n",
       "1341     2011  Volkswagen      Golf     Auckland           32800  Hatchback   \n",
       "1342     2011  Volkswagen      Golf     Auckland           32800  Hatchback   \n",
       "5532     2004      Nissan    Murano     Auckland          138452     RV/SUV   \n",
       "5533     2004      Nissan    Murano     Auckland          138452     RV/SUV   \n",
       "\n",
       "       Car_Gear Car_Seat Car_Fuel(cc) Car_Door_Num Car_Wof_Exp Car_Fuel_Type  \\\n",
       "593   Tiptronic      NaN         2500          NaN    Apr 2022        Petrol   \n",
       "594   Tiptronic      NaN         2500          NaN         NaN        Petrol   \n",
       "1341  Automatic        5         1389          NaN    Mar 2022        Petrol   \n",
       "1342  Automatic        5         1389          NaN         NaN        Petrol   \n",
       "5532  Automatic        5         3498            5         NaN        Petrol   \n",
       "5533  Automatic        5         3498            5         NaN        Petrol   \n",
       "\n",
       "       Car_Reg Car_Color  \n",
       "593        NaN     White  \n",
       "594        NaN     White  \n",
       "1341  May 2021       Red  \n",
       "1342  May 2021       Red  \n",
       "5532       NaN      Gold  \n",
       "5533       NaN      Gold  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[(Car_data_target['Car_No'] == '89038') | (Car_data_target['Car_No'] == '85695') | (Car_data_target['Car_No'] == '75514')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop duplicate records "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target = Car_data_target.drop(Car_data_target[(Car_data_target['Car_No'] == '89038') & (Car_data_target['Car_Wof_Exp'].isnull() == True)].index)\n",
    "Car_data_target = Car_data_target.drop(Car_data_target[(Car_data_target['Car_No'] == '85695') & (Car_data_target['Car_Wof_Exp'].isnull() == True)].index)\n",
    "Car_data_target.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13439, 18)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Wof_Exp</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Reg</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Car_No, Car_Price, Car_ORC, Car_Title, Car_Year, Car_Brand, Car_Model, Car_Location, Car_Mileage(km), Car_Type, Car_Gear, Car_Seat, Car_Fuel(cc), Car_Door_Num, Car_Wof_Exp, Car_Fuel_Type, Car_Reg, Car_Color]\n",
       "Index: []"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[(Car_data_target.duplicated(['Car_No']) == True)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_No                 0\n",
       "Car_Price            863\n",
       "Car_ORC                0\n",
       "Car_Title              0\n",
       "Car_Year               0\n",
       "Car_Brand              0\n",
       "Car_Model             91\n",
       "Car_Location           0\n",
       "Car_Mileage(km)        0\n",
       "Car_Type               1\n",
       "Car_Gear              33\n",
       "Car_Seat            2645\n",
       "Car_Fuel(cc)         725\n",
       "Car_Door_Num        2531\n",
       "Car_Wof_Exp        10634\n",
       "Car_Fuel_Type        496\n",
       "Car_Reg            12889\n",
       "Car_Color           1608\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing value percentage for each columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Reg            0.959074\n",
       "Car_Wof_Exp        0.791279\n",
       "Car_Seat           0.196815\n",
       "Car_Door_Num       0.188332\n",
       "Car_Color          0.119652\n",
       "Car_Price          0.064216\n",
       "Car_Fuel(cc)       0.053947\n",
       "Car_Fuel_Type      0.036908\n",
       "Car_Model          0.006771\n",
       "Car_Gear           0.002456\n",
       "Car_Type           0.000074\n",
       "Car_Title          0.000000\n",
       "Car_ORC            0.000000\n",
       "Car_Mileage(km)    0.000000\n",
       "Car_Year           0.000000\n",
       "Car_Brand          0.000000\n",
       "Car_Location       0.000000\n",
       "Car_No             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target.isnull().sum()/len(Car_data_target))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop columns which missing percentage larger than 50%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop columns which missing percentage larger than 50% \n",
    "# drop Car_Reg and Wof_Exp \n",
    "Car_data_target.drop(['Car_Reg'], axis=1, inplace=True)\n",
    "\n",
    "Car_data_target.drop(['Car_Wof_Exp'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assign Car_Seat and Car_Door_Num according to Car_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      NaN\n",
       "2        5\n",
       "6        4\n",
       "63       2\n",
       "122      3\n",
       "Name: Car_Door_Num, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target['Car_Door_Num'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                5\n",
       "1                7\n",
       "4                8\n",
       "18             NaN\n",
       "31               4\n",
       "42               3\n",
       "46               2\n",
       "425              6\n",
       "1536    10 or more\n",
       "Name: Car_Seat, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target['Car_Seat'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target['Car_Seat'] = Car_data_target['Car_Seat'].replace('10 or more', '10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Car_data_target[(Car_data_target['Car_Seat'] == '10 or more')]\n",
    "\n",
    "Car_data_target['Car_Seat'].fillna(value = 0, inplace=True)\n",
    "Car_data_target['Car_Door_Num'].fillna(value = 0, inplace=True)\n",
    "Car_data_target['Car_Fuel(cc)'].fillna(value = 0, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5\n",
       "1        7\n",
       "4        8\n",
       "18       0\n",
       "31       4\n",
       "42       3\n",
       "46       2\n",
       "425      6\n",
       "1536    10\n",
       "Name: Car_Seat, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target['Car_Seat'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86261</td>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005 Nissan Tiida Axis</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Tiida</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79395</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009 Toyota Wish</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Wish</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7</td>\n",
       "      <td>1800</td>\n",
       "      <td>0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85473</td>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>2400</td>\n",
       "      <td>5</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88342</td>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>2400</td>\n",
       "      <td>5</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72923</td>\n",
       "      <td>9900</td>\n",
       "      <td>1</td>\n",
       "      <td>2012 Nissan Serena 20X</td>\n",
       "      <td>2012</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Serena</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107126</td>\n",
       "      <td>Van</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>8</td>\n",
       "      <td>2000</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Light Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_No Car_Price Car_ORC                                          Car_Title  \\\n",
       "0  86261      3997       0                             2005 Nissan Tiida Axis   \n",
       "1  79395      7997       0                                   2009 Toyota Wish   \n",
       "2  85473     9940        1  2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...   \n",
       "3  88342     8940        1  2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...   \n",
       "4  72923     9900        1                             2012 Nissan Serena 20X   \n",
       "\n",
       "  Car_Year   Car_Brand  Car_Model Car_Location Car_Mileage(km)       Car_Type  \\\n",
       "0     2005      Nissan      Tiida      Dunedin          138150      Hatchback   \n",
       "1     2009      Toyota       Wish      Dunedin          100300  Station Wagon   \n",
       "2     2007  Mitsubishi  OUTLANDER     Auckland          102425         RV/SUV   \n",
       "3     2006  Mitsubishi  OUTLANDER     Auckland          109425         RV/SUV   \n",
       "4     2012      Nissan     Serena     Auckland          107126            Van   \n",
       "\n",
       "    Car_Gear Car_Seat Car_Fuel(cc) Car_Door_Num Car_Fuel_Type   Car_Color  \n",
       "0  Automatic        5         1500            0        Petrol      Silver  \n",
       "1  Automatic        7         1800            0        Petrol       White  \n",
       "2  Automatic        5         2400            5        Petrol      Silver  \n",
       "3  Automatic        5         2400            5        Petrol       Black  \n",
       "4  Automatic        8         2000            5           NaN  Light Blue  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tiida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>TIIDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4145</th>\n",
       "      <td>tiida</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Car_Model\n",
       "0        Tiida\n",
       "75       TIIDA\n",
       "4145     tiida"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[['Car_Model']][(Car_data_target['Car_Model'].str.upper() == 'TIIDA')].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert data type from object to int\n",
    "Car_data_target['Car_Seat'] = Car_data_target['Car_Seat'].astype(int)\n",
    "\n",
    "Car_data_target['Car_Door_Num'] = Car_data_target['Car_Door_Num'].astype(int)\n",
    "\n",
    "Car_data_target['Car_Fuel(cc)'] = Car_data_target['Car_Fuel(cc)'].astype(int)\n",
    "\n",
    "Car_data_target['Car_Fuel(cc)'] = Car_data_target['Car_Fuel(cc)'].astype(int)\n",
    "\n",
    "\n",
    "Car_data_target['Car_Model_upper'] = Car_data_target['Car_Model'].str.upper()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Get median door number and seat for each brand and model\n",
    "\n",
    "Car_median_seat = Car_data_target[(Car_data_target['Car_Seat'] != 0)].groupby(['Car_Brand', 'Car_Model_upper', 'Car_Type'])[['Car_Seat']].median().reset_index()\n",
    "\n",
    "Car_median_door = Car_data_target[(Car_data_target['Car_Door_Num'] != 0)].groupby(['Car_Brand', 'Car_Model_upper', 'Car_Type'])[['Car_Door_Num']].median().reset_index()\n",
    "\n",
    "Car_median_fuel = Car_data_target[(Car_data_target['Car_Fuel(cc)'] != 0)].groupby(['Car_Brand', 'Car_Model_upper', 'Car_Type'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "  \n",
    "# Convert to integer format\n",
    "    \n",
    "Car_median_seat['Car_Seat'] = Car_median_seat['Car_Seat'].astype(int)\n",
    "\n",
    "Car_median_door['Car_Door_Num'] = Car_median_door['Car_Door_Num'].astype(int)\n",
    "\n",
    "Car_median_fuel['Car_Fuel(cc)'] = Car_median_fuel['Car_Fuel(cc)'].astype(int)\n",
    " \n",
    "#,'Car_Seat', 'Car_Door_Num']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model_upper</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aston</td>\n",
       "      <td>MARTIN</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>4735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aston</td>\n",
       "      <td>MARTIN</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Audi</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>1.8T</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>1798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Brand Car_Model_upper       Car_Type  Car_Fuel(cc)\n",
       "0     Aston          MARTIN          Coupe          4735\n",
       "1     Aston          MARTIN          Sedan          6000\n",
       "2      Audi             1.8          Sedan          1800\n",
       "3      Audi            1.8T  Station Wagon          1798\n",
       "4      Audi             2.0          Coupe          1984"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_median_fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1506 entries, 0 to 1505\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Car_Brand        1506 non-null   object\n",
      " 1   Car_Model_upper  1506 non-null   object\n",
      " 2   Car_Type         1506 non-null   object\n",
      " 3   Car_Door_Num     1506 non-null   int32 \n",
      "dtypes: int32(1), object(3)\n",
      "memory usage: 23.6+ KB\n"
     ]
    }
   ],
   "source": [
    "Car_median_door.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat = pd.merge(Car_data_target, Car_median_seat, on=['Car_Brand', 'Car_Model_upper', 'Car_Type'], how = 'left')\n",
    "\n",
    "# Assign median seat number if seat number is null \n",
    "Car_data_target_seat.loc[Car_data_target_seat['Car_Seat_x'] == 0, 'Car_Seat_x'] = Car_data_target_seat.loc[Car_data_target_seat['Car_Seat_x'] == 0, 'Car_Seat_y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18       5.0\n",
       "20       7.0\n",
       "33       7.0\n",
       "37       5.0\n",
       "44       NaN\n",
       "        ... \n",
       "13386    5.0\n",
       "13390    5.0\n",
       "13392    4.0\n",
       "13416    5.0\n",
       "13434    7.0\n",
       "Name: Car_Seat_x, Length: 2645, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat.loc[Car_data_target[(Car_data_target['Car_Seat'] == 0)].index, 'Car_Seat_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18       5.0\n",
       "20       7.0\n",
       "33       7.0\n",
       "37       5.0\n",
       "44       NaN\n",
       "        ... \n",
       "13386    5.0\n",
       "13390    5.0\n",
       "13392    4.0\n",
       "13416    5.0\n",
       "13434    7.0\n",
       "Name: Car_Seat_y, Length: 2645, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat.loc[Car_data_target[(Car_data_target['Car_Seat'] == 0)].index, 'Car_Seat_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door = pd.merge(Car_data_target_seat, Car_median_door, on=['Car_Brand', 'Car_Model_upper', 'Car_Type'], how = 'left')\n",
    "\n",
    "# Assign median door number if seat number is null \n",
    "Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_x'] = Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13439, 19)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5.0\n",
       "1        5.0\n",
       "20       5.0\n",
       "33       5.0\n",
       "35       4.0\n",
       "        ... \n",
       "13400    2.0\n",
       "13406    4.0\n",
       "13413    5.0\n",
       "13416    5.0\n",
       "13434    5.0\n",
       "Name: Car_Door_Num_x, Length: 2531, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5.0\n",
       "1        5.0\n",
       "20       5.0\n",
       "33       5.0\n",
       "35       4.0\n",
       "        ... \n",
       "13400    2.0\n",
       "13406    4.0\n",
       "13413    5.0\n",
       "13416    5.0\n",
       "13434    5.0\n",
       "Name: Car_Door_Num_y, Length: 2531, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door_fuel = pd.merge(Car_data_target_seat_door, Car_median_fuel, on=['Car_Brand', 'Car_Model_upper', 'Car_Type'], how = 'left')\n",
    "\n",
    "# Assign median door number if seat number is null \n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_x'] = Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44          NaN\n",
       "127         NaN\n",
       "138      2350.0\n",
       "167      1240.0\n",
       "218      2400.0\n",
       "          ...  \n",
       "13371    1500.0\n",
       "13372    1800.0\n",
       "13374    1500.0\n",
       "13390    3498.0\n",
       "13396    2000.0\n",
       "Name: Car_Fuel(cc)_x, Length: 725, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44          NaN\n",
       "127         NaN\n",
       "138      2350.0\n",
       "167      1240.0\n",
       "218      2400.0\n",
       "          ...  \n",
       "13371    1500.0\n",
       "13372    1800.0\n",
       "13374    1500.0\n",
       "13390    3498.0\n",
       "13396    2000.0\n",
       "Name: Car_Fuel(cc)_y, Length: 725, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_columns = ['Car_No', 'Car_Price', 'Car_ORC', 'Car_Title', 'Car_Year', 'Car_Brand',\n",
    "       'Car_Model', 'Car_Location', 'Car_Mileage(km)', 'Car_Type', 'Car_Gear',\n",
    "       'Car_Seat', 'Car_Fuel(cc)', 'Car_Door_Num', 'Car_Fuel_Type',\n",
    "       'Car_Color']\n",
    "Car_data_target_seat_door_fuel.drop(['Car_Seat_y', 'Car_Door_Num_y', 'Car_Model_upper', 'Car_Fuel(cc)_y'], axis=1, inplace=True) \n",
    "Car_data_target_seat_door_fuel.columns = data_columns \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86261</td>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005 Nissan Tiida Axis</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Tiida</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79395</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009 Toyota Wish</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Wish</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85473</td>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88342</td>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72923</td>\n",
       "      <td>9900</td>\n",
       "      <td>1</td>\n",
       "      <td>2012 Nissan Serena 20X</td>\n",
       "      <td>2012</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Serena</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107126</td>\n",
       "      <td>Van</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Light Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_No Car_Price Car_ORC                                          Car_Title  \\\n",
       "0  86261      3997       0                             2005 Nissan Tiida Axis   \n",
       "1  79395      7997       0                                   2009 Toyota Wish   \n",
       "2  85473     9940        1  2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...   \n",
       "3  88342     8940        1  2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...   \n",
       "4  72923     9900        1                             2012 Nissan Serena 20X   \n",
       "\n",
       "  Car_Year   Car_Brand  Car_Model Car_Location Car_Mileage(km)       Car_Type  \\\n",
       "0     2005      Nissan      Tiida      Dunedin          138150      Hatchback   \n",
       "1     2009      Toyota       Wish      Dunedin          100300  Station Wagon   \n",
       "2     2007  Mitsubishi  OUTLANDER     Auckland          102425         RV/SUV   \n",
       "3     2006  Mitsubishi  OUTLANDER     Auckland          109425         RV/SUV   \n",
       "4     2012      Nissan     Serena     Auckland          107126            Van   \n",
       "\n",
       "    Car_Gear  Car_Seat  Car_Fuel(cc)  Car_Door_Num Car_Fuel_Type   Car_Color  \n",
       "0  Automatic       5.0        1500.0           5.0        Petrol      Silver  \n",
       "1  Automatic       7.0        1800.0           5.0        Petrol       White  \n",
       "2  Automatic       5.0        2400.0           5.0        Petrol      Silver  \n",
       "3  Automatic       5.0        2400.0           5.0        Petrol       Black  \n",
       "4  Automatic       8.0        2000.0           5.0           NaN  Light Blue  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>1990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2455</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2708</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3804</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4265</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4448</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4562</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9542</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11500</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12776</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Car_Brand  Car_Model       Car_Type  Car_Fuel(cc)\n",
       "2      Mitsubishi  OUTLANDER         RV/SUV        2400.0\n",
       "64     Mitsubishi  Outlander         RV/SUV        2400.0\n",
       "85     Mitsubishi  Outlander         RV/SUV        2359.0\n",
       "175    Mitsubishi  Outlander  Station Wagon        2359.0\n",
       "273    Mitsubishi  Outlander  Station Wagon        2997.0\n",
       "331    Mitsubishi  Outlander         RV/SUV        2352.0\n",
       "592    Mitsubishi  Outlander         RV/SUV        1990.0\n",
       "723    Mitsubishi  OUTLANDER  Station Wagon        2400.0\n",
       "1049   Mitsubishi  Outlander         RV/SUV           0.0\n",
       "1147   Mitsubishi  Outlander         RV/SUV        2000.0\n",
       "1671   Mitsubishi  Outlander  Station Wagon        1998.0\n",
       "2156   Mitsubishi  Outlander         RV/SUV        2300.0\n",
       "2455   Mitsubishi  Outlander  Station Wagon           0.0\n",
       "2708   Mitsubishi  Outlander         RV/SUV        2350.0\n",
       "3804   Mitsubishi  Outlander         RV/SUV        2360.0\n",
       "4265   Mitsubishi  Outlander  Station Wagon        2400.0\n",
       "4448   Mitsubishi  Outlander  Station Wagon        2379.0\n",
       "4562   Mitsubishi  Outlander  Station Wagon        2350.0\n",
       "9542   Mitsubishi  OUTLANDER         RV/SUV        2350.0\n",
       "11500  Mitsubishi  OUTLANDER         RV/SUV        2356.0\n",
       "12776  Mitsubishi  OUTLANDER         RV/SUV           0.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Car_data_target_seat_door.groupby(['Car_Brand', 'Car_Model', 'Car_Type'])[['Car_Fuel(cc)']]\n",
    "\n",
    "Car_data_target_seat_door_fuel[['Car_Brand', 'Car_Model', 'Car_Type', 'Car_Fuel(cc)']][(Car_data_target_seat_door['Car_Model'].str.upper() == 'OUTLANDER')].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13439 entries, 0 to 13438\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Car_No           13439 non-null  object \n",
      " 1   Car_Price        12576 non-null  object \n",
      " 2   Car_ORC          13439 non-null  object \n",
      " 3   Car_Title        13439 non-null  object \n",
      " 4   Car_Year         13439 non-null  object \n",
      " 5   Car_Brand        13439 non-null  object \n",
      " 6   Car_Model        13348 non-null  object \n",
      " 7   Car_Location     13439 non-null  object \n",
      " 8   Car_Mileage(km)  13439 non-null  object \n",
      " 9   Car_Type         13438 non-null  object \n",
      " 10  Car_Gear         13406 non-null  object \n",
      " 11  Car_Seat         13159 non-null  float64\n",
      " 12  Car_Fuel(cc)     13410 non-null  float64\n",
      " 13  Car_Door_Num     13351 non-null  float64\n",
      " 14  Car_Fuel_Type    12943 non-null  object \n",
      " 15  Car_Color        11831 non-null  object \n",
      "dtypes: float64(3), object(13)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Color          0.119652\n",
       "Car_Price          0.064216\n",
       "Car_Fuel_Type      0.036908\n",
       "Car_Seat           0.020835\n",
       "Car_Model          0.006771\n",
       "Car_Door_Num       0.006548\n",
       "Car_Gear           0.002456\n",
       "Car_Fuel(cc)       0.002158\n",
       "Car_Type           0.000074\n",
       "Car_Mileage(km)    0.000000\n",
       "Car_Location       0.000000\n",
       "Car_Brand          0.000000\n",
       "Car_Year           0.000000\n",
       "Car_Title          0.000000\n",
       "Car_ORC            0.000000\n",
       "Car_No             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target_seat_door_fuel.isnull().sum()/len(Car_data_target_seat_door))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop rows with null price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door_fuel.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Color          0.0\n",
       "Car_Fuel_Type      0.0\n",
       "Car_Door_Num       0.0\n",
       "Car_Fuel(cc)       0.0\n",
       "Car_Seat           0.0\n",
       "Car_Gear           0.0\n",
       "Car_Type           0.0\n",
       "Car_Mileage(km)    0.0\n",
       "Car_Location       0.0\n",
       "Car_Model          0.0\n",
       "Car_Brand          0.0\n",
       "Car_Year           0.0\n",
       "Car_Title          0.0\n",
       "Car_ORC            0.0\n",
       "Car_Price          0.0\n",
       "Car_No             0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target_seat_door_fuel.isnull().sum()/len(Car_data_target_seat_door))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10360, 16)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete records with P.O.A price\n",
    "Car_data_target_seat_door_fuel.drop(Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Price'] == 'P.0.A')].index, axis=0, inplace = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Color          0.0\n",
       "Car_Fuel_Type      0.0\n",
       "Car_Door_Num       0.0\n",
       "Car_Fuel(cc)       0.0\n",
       "Car_Seat           0.0\n",
       "Car_Gear           0.0\n",
       "Car_Type           0.0\n",
       "Car_Mileage(km)    0.0\n",
       "Car_Location       0.0\n",
       "Car_Model          0.0\n",
       "Car_Brand          0.0\n",
       "Car_Year           0.0\n",
       "Car_Title          0.0\n",
       "Car_ORC            0.0\n",
       "Car_Price          0.0\n",
       "Car_No             0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target_seat_door_fuel.isnull().sum()/len(Car_data_target_seat_door))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to integer for numeric columns \n",
    "Car_data_target_seat_door_fuel['Car_Price'] = Car_data_target_seat_door_fuel['Car_Price'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Year'] = Car_data_target_seat_door_fuel['Car_Year'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Mileage(km)'] = Car_data_target_seat_door_fuel['Car_Mileage(km)'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Fuel(cc)'] = Car_data_target_seat_door_fuel['Car_Fuel(cc)'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Price'] = Car_data_target_seat_door_fuel['Car_Price'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_ORC'] = Car_data_target_seat_door_fuel['Car_ORC'].astype('int')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10069, 16)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10069 entries, 0 to 13438\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Car_No           10069 non-null  object \n",
      " 1   Car_Price        10069 non-null  int32  \n",
      " 2   Car_ORC          10069 non-null  int32  \n",
      " 3   Car_Title        10069 non-null  object \n",
      " 4   Car_Year         10069 non-null  int32  \n",
      " 5   Car_Brand        10069 non-null  object \n",
      " 6   Car_Model        10069 non-null  object \n",
      " 7   Car_Location     10069 non-null  object \n",
      " 8   Car_Mileage(km)  10069 non-null  int32  \n",
      " 9   Car_Type         10069 non-null  object \n",
      " 10  Car_Gear         10069 non-null  object \n",
      " 11  Car_Seat         10069 non-null  float64\n",
      " 12  Car_Fuel(cc)     10069 non-null  int32  \n",
      " 13  Car_Door_Num     10069 non-null  float64\n",
      " 14  Car_Fuel_Type    10069 non-null  object \n",
      " 15  Car_Color        10069 non-null  object \n",
      "dtypes: float64(2), int32(5), object(9)\n",
      "memory usage: 786.6+ KB\n"
     ]
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Car Fuel(cc)')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Car_data_target_seat_door_fuel['Car_Fuel(cc)'])\n",
    "plt.title('Distribution of Car Fuel(cc)', fontsize = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete Outlier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>76494</td>\n",
       "      <td>6950</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Honda Odyssey</td>\n",
       "      <td>2006</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Odyssey</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>111889</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>111889</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4006</th>\n",
       "      <td>76004</td>\n",
       "      <td>17495</td>\n",
       "      <td>1</td>\n",
       "      <td>2011 Audi Cabriolet</td>\n",
       "      <td>2011</td>\n",
       "      <td>Audi</td>\n",
       "      <td>Cabriolet</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>57325</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Grey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10963</th>\n",
       "      <td>31643</td>\n",
       "      <td>9440</td>\n",
       "      <td>1</td>\n",
       "      <td>2008 Toyota Auris</td>\n",
       "      <td>2008</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Auris</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>98000</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>98000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12884</th>\n",
       "      <td>23440</td>\n",
       "      <td>12990</td>\n",
       "      <td>1</td>\n",
       "      <td>2008 Toyota Harrier</td>\n",
       "      <td>2008</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Harrier</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>97500</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23600</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Car_No  Car_Price  Car_ORC            Car_Title  Car_Year Car_Brand  \\\n",
       "3999   76494       6950        1   2006 Honda Odyssey      2006     Honda   \n",
       "4006   76004      17495        1  2011 Audi Cabriolet      2011      Audi   \n",
       "10963  31643       9440        1    2008 Toyota Auris      2008    Toyota   \n",
       "12884  23440      12990        1  2008 Toyota Harrier      2008    Toyota   \n",
       "\n",
       "       Car_Model Car_Location  Car_Mileage(km)       Car_Type   Car_Gear  \\\n",
       "3999     Odyssey     Auckland           111889  Station Wagon  Automatic   \n",
       "4006   Cabriolet     Auckland            57325    Convertible  Tiptronic   \n",
       "10963      Auris     Auckland            98000      Hatchback  Automatic   \n",
       "12884    Harrier     Auckland            97500         RV/SUV  Automatic   \n",
       "\n",
       "       Car_Seat  Car_Fuel(cc)  Car_Door_Num Car_Fuel_Type Car_Color  \n",
       "3999        7.0        111889           5.0        Petrol     White  \n",
       "4006        4.0         20000           2.0        Petrol      Grey  \n",
       "10963       5.0         98000           5.0        Petrol       Red  \n",
       "12884       5.0         23600           4.0        Petrol     Black  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Fuel(cc)'] > 10000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete 76004\n",
    "\n",
    "Car_data_target_seat_door_fuel.drop(Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '76004')].index, axis=0, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_Odyssey = Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Model'] == 'Odyssey') & (Car_data_target_seat_door_fuel['Car_Type'] == 'Station Wagon') & (Car_data_target_seat_door_fuel['Car_Brand'] == 'Honda')].groupby(['Car_Model', 'Car_Type', 'Car_Brand'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "\n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '76494')].index, 'Car_Fuel(cc)'] = median_Odyssey.loc[0, 'Car_Fuel(cc)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_Auris = Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Model'] == 'Auris') & (Car_data_target_seat_door_fuel['Car_Type'] == 'Hatchback') & (Car_data_target_seat_door_fuel['Car_Brand'] == 'Toyota')].groupby(['Car_Model', 'Car_Type'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '31643')].index, 'Car_Fuel(cc)'] = median_Auris.loc[0, 'Car_Fuel(cc)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_Harrier = Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Model'] == 'Harrier') & (Car_data_target_seat_door_fuel['Car_Type'] == 'RV/SUV') & (Car_data_target_seat_door_fuel['Car_Brand'] == 'Toyota')].groupby(['Car_Model', 'Car_Type', 'Car_Brand'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '23440')].index, 'Car_Fuel(cc)'] = median_Harrier.loc[0, 'Car_Fuel(cc)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Car Fuel(cc)')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Car_data_target_seat_door_fuel['Car_Fuel(cc)'])\n",
    "plt.title('Distribution of Car Fuel(cc)', fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Car Mileage(km)')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Car_data_target_seat_door_fuel['Car_Mileage(km)'])\n",
    "plt.title('Distribution of Car Mileage(km)', fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xlLK+ZkkfIVyG8Ze4X9cTPsQTCNfVvZ7wY6UzXbwtJLwfv5d0E6HR0dNm1plzalcRftjslzJ9HqHh08cljQcWE16j3PWWH+3EtstxB/CZ+KPrn+y4Tq4G+Gx+tbOZzZZ0IOFc0BOSbiE0lhhB+HF1KKFm6XOdLZSZ/Sb+OPsoYV/6I6Ha8+i4rTlm9uvObodwtH2OpAcIn811hMT/VkIjkRbgc9b2GuNPEBqNXSnpC4T3biPhqH1/wvfoIey4fvgiQsO6B+J3bTPh+2Vv0j+XC4FZkv5EaEzUAtxhZncQrpPcJulOQgIV4bP1tjjv34o+4440yayWG6FFz08JvzpeJNQDryEcwZ3MzteOvT2+WRsIyeBOwk50GEWaMHewbCspsXkvRa47y5snsYkt4YfK5wm/lHO9JCwnNBOeXDCvCBd8LyT09vAKIdHdSeizbkKJz61oeQnnCS8jtGx7hdBC7Y8k99jR7nNvpyyTCSe5N8bnfhdl9HjS3utbbJm86fsRvuSfJrS4W8+Oi43fU+4+Rfjh8f24jpfj+7o8Ps8TaHv91zmx7IclrGdSnHZVQbwf4RrLJ9nRlDzxuae8VztdrhCnz2BHs/TrEqZPAG6Ir89WwvVvH6EDnz+KX5Kw02tC2x5P3kxIrBvi6/tPYEaR5/0hwjWBz8f9+TnC5+07wJtK3Y9KeH1rCAl1SSzXy4Qv8dNI6JGo2GtQZBvvIFyofSchYTcRPjePEzo12C9luSGE74j74v64lXCN4l8ILWt3TdhX6uO6XyAcIe+Xtr8S9vnfEBoGbc/fHwg/Iv4Q99dcbz4PAGeQcDlI4U1xJc4551yv0+POyTnnnHOl8iTnnHOu1/Ik55xzrtfyJOecc67X6pGXEFTKqFGjbNKkSVkXwznnepT77rvvBTMb3f6c3c+TXBGTJk1iyZKi3aI555wrIKmwY+zMeHWlc865XsuTnHPOuV7Lk5xzzrley5Occ865XsuTnHPOuV4rkyQnqZ+kB2Kv+UgaIWmBpOXxfnjevGdJWiHp8TgsTi5+oKSH4rRL4lAOSBog6YYYX5w/rISkk+I2lktKHSS1KzRuaeLBVRtp3NLU/szOOee6RVZHcqcD+QPqfRVYaGZTCD3kfxUgjm82C9iHMCTOpXkDPV5G6P16SrwdGeMnAxvMbDJhyIcL4rpGEIbmORg4CDg7P5l2pXn1q5l+wSJOuGIx0y9YxPz61d2xGeecc+2oeJKLY0l9ELgiLzwTuDo+vpow/E0ufr2ZNZnZU8AK4CBJY4HdzOwuC8MoXFOwTG5dNwKHx6O8GcACM1tvZhsIA27mEmOXadzSxJlzl7KtuZXNTS1sa27ljLlL/YjOOecykMWR3MWEcYBa82JjzGwNQLzPjfg6DliVN19DjI2LjwvjbZaxMCL2JmBkkXW1IekUSUskLVm3bl3ZT65hw1Zqa9q+rLU1NTRs2Fr2upxzznVORZOcpA8Bz5vZfaUukhCzIvGOLrMjYHa5mdWZWd3o0eX3SjN++CCaW1vbxJpbWxk/fFDZ63LOOdc5lT6Smw4cJWklYUjz90i6DlgbqyCJ97lh1BsIownnjAeejfHxCfE2y0jqTxjafX2RdXWpkYMHcOGx+zOwtoYhA/ozsLaGC4/dn5GDB3T1ppxzzrUjs5HBJR0G/K+ZfUjSD4BGM/u+pK8CI8zsDEn7EIZEPwjYg9AoZYqZbZd0L/DfwGLgJuCnZnaTpNMIQ7h/TtIs4CNm9tHY8OQ+4K2xCPcDB5rZ+rQy1tXVWUf7rmzc0kTDhq2MHz7IE5xzrk+RdJ+Z1WVdDqieDpq/D8yRdDLwDHA8gJktkzQHeARoAU4zs+1xmVOBq4BBwM3xBnAlcK2kFYQjuFlxXeslnQfcG+c7t1iC66yRgwd4cnPOuYxldiTXE3TmSM455/qqajqS8x5PnHPO9Vqe5JxzzvVanuScc871Wp7knHPO9Vqe5JxzzvVanuScc871Wp7knHPO9Vqe5JxzzvVanuScc871Wp7knHPO9Vqe5JxzzvVanuScc871Wp7kuknjliYeXLWRxi1NWRfFOef6rGoZaqdXmVe/mjPnLqW2pobm1lYuPHZ/jpo6LutiOedcn+NHcl2scUsTZ85dyrbmVjY3tbCtuZUz5i71IzrnnMuAJ7ku1rBhK7U1bV/W2poaGjZszahEzjnXd1U0yUkaKOkeSQ9KWibp2zF+jqTVkurj7QN5y5wlaYWkxyXNyIsfKOmhOO0SSYrxAZJuiPHFkiblLXOSpOXxdlJ3PMfxwwfR3NraJtbc2sr44YO6Y3POOeeKqPSRXBPwHjM7AJgKHClpWpx2kZlNjbebACTtDcwC9gGOBC6V1C/OfxlwCjAl3o6M8ZOBDWY2GbgIuCCuawRwNnAwcBBwtqThXf0ERw4ewIXH7s/A2hqGDOjPwNoaLjx2f0YOHtDVm3LOOdeOijY8MTMDtsR/a+PNiiwyE7jezJqApyStAA6StBLYzczuApB0DXA0cHNc5py4/I3Az+JR3gxggZmtj8ssICTG33bZE4yOmjqO6ZNH0bBhK+OHD/IE55xzGan4OTlJ/STVA88Tks7iOOnzkpZKmp13hDUOWJW3eEOMjYuPC+NtljGzFmATMLLIugrLd4qkJZKWrFu3rsPPc+TgARwwYZgnOOecy1DFk5yZbTezqcB4wlHZvoSqxzcQqjDXAD+KsytpFUXiHV0mv3yXm1mdmdWNHj266HNxzjlX3TJrXWlmG4HbgCPNbG1Mfq3ALwnnzCAcbU3IW2w88GyMj0+It1lGUn9gKLC+yLpcFfCL551z3aHSrStHSxoWHw8C3gs8Jmls3mzHAA/Hx/OBWbHF5J6EBib3mNkaYLOkafF824nAvLxlci0njwMWxXOBtwBHSBoeq0OPiDGXsXn1q5l+wSJOuGIx0y9YxPz61VkXyTnXS1S6x5OxwNWxhWQNMMfM/izpWklTCdWHK4HPApjZMklzgEeAFuA0M9se13UqcBUwiNDg5OYYvxK4NjZSWU9onYmZrZd0HnBvnO/cXCMUl538i+e3ES69OGPuUqZPHuXnM51znVbp1pVLgbckxD9ZZJnzgfMT4kuAfRPi24DjU9Y1G5hdRpFdN8tdPJ9LcLDj4nlPcs65zvIeT1ym/OJ551x38iTnMuUXzzvnupOPQuAy5xfPO+e6iyc5VxVGDh7gyc051+W8utI551yv5UnOOedcr+VJzjnnXK/lSc4551yv5UnOuSK8T03nejZvXelcinn1qzlz7lJqa2pobm3lwmP356ipO43O5JyrYn4k51yC/D41Nze1sK25lTPmLvUjOud6GE9yziXI9amZL9enpnOu5/Ak51wC71PTud7Bk5xzCbxPTed6B2944lwK71PTuZ7Pk5xzRXifms71bBWtrpQ0UNI9kh6UtEzSt2N8hKQFkpbH++F5y5wlaYWkxyXNyIsfKOmhOO0SSYrxAZJuiPHFkiblLXNS3MZySSdV7pk755zLQqXPyTUB7zGzA4CpwJGSpgFfBRaa2RRgYfwfSXsDs4B9gCOBSyX1i+u6DDgFmBJvR8b4ycAGM5sMXARcENc1AjgbOBg4CDg7P5k655zrfSqa5CzYEv+tjTcDZgJXx/jVwNHx8UzgejNrMrOngBXAQZLGAruZ2V1mZsA1Bcvk1nUjcHg8ypsBLDCz9Wa2AVjAjsTonHOuF6p460pJ/STVA88Tks5iYIyZrQGI97vH2ccBq/IWb4ixcfFxYbzNMmbWAmwCRhZZV2H5TpG0RNKSdevWdeapOuecy1jFk5yZbTezqcB4wlHZvkVmV9IqisQ7ukx++S43szozqxs9enSRojnnnKt2mV0nZ2YbgdsIVYZrYxUk8f75OFsDMCFvsfHAszE+PiHeZhlJ/YGhwPoi6+oW3rGvc85lr9KtK0dLGhYfDwLeCzwGzAdyrR1PAubFx/OBWbHF5J6EBib3xCrNzZKmxfNtJxYsk1vXccCieN7uFuAIScNjg5MjYqzLzatfzfQLFnHCFYuZfsEi5tev7o7NOOeca0elr5MbC1wdW0jWAHPM7M+S7gLmSDoZeAY4HsDMlkmaAzwCtACnmdn2uK5TgauAQcDN8QZwJXCtpBWEI7hZcV3rJZ0H3BvnO9fM1nf1E8zv2HcboVuoM+YuZfrkUX69lXPOVVhFk5yZLQXekhBvBA5PWeZ84PyE+BJgp/N5ZraNmCQTps0GZpdX6vLkOvbNJTjY0bGvJznnnKss77uyi3nHvs45Vz08yXUx79i3d/EGRM71bN53ZTfwjn17Bx8Z3Lmez5NcN/GOfXs2b0DkXO/g1ZXOJfCRwZ3rHTzJOZfAGxA51zt4knMugTcgcq538HNyzqXwBkTO9Xye5JwrwhsQOdezeXWlc865XsuTnHNF+MXgzvVsXl3pXAq/GNy5nq/kI7k43M2pkq6UdKukKTH+MUlv7r4iOld5+ReDb25qYVtzK2fMXepHdM71MCUlOUl7Af8GvgdMIowYMCROfidwVncUzrms+MXgzvUOpR7JXUIY520SMANQ3rTbgXd0bbGcy5ZfDO5c71Bqknsn8D0z2whYwbS1hMFQ2yVpgqS/S3pU0jJJp8f4OZJWS6qPtw/kLXOWpBWSHpc0Iy9+oKSH4rRL4gjhuWrVG2J8saRJecucJGl5vJ2Ecyn8YnDneodSG55sI4zAnWQcsLHE9bQAXzaz+yUNAe6TtCBOu8jMfpg/s6S9CSN77wPsAfxN0l5xdPDLgFOAu4GbgCMJo4OfDGwws8mSZgEXAB+TNAI4G6gjJOr7JM03sw0llt31MX4xuHM9X6lHcguAr0kamhczSQOA/yYkmXaZ2Rozuz8+3gw8SkiSaWYC15tZk5k9BawADpI0FtjNzO4yMwOuAY7OW+bq+PhG4PB4lDcDWGBm62NiW0BIjM6lGjl4AAdMGOYJzrkeqtQk9xVgNCHJXEs4EvoW8BDhCOvr5W44ViO+BVgcQ5+XtFTSbEnDY2wcsCpvsYYYGxcfF8bbLGNmLcAmYGSRdRWW6xRJSyQtWbduXblPyznnXBUpKcmZ2SrgAOD/CI1PniCch/sdcKCZPVfORiUNBuYCXzSzFwlVj28ApgJrgB/lZk0qTpF4R5fZETC73MzqzKxu9OjRRZ+Hc8656lbyxeCxiu+b8dZhkmoJCe7XZvb7uO61edN/Cfw5/tsATMhbfDzwbIyPT4jnL9MgqT8wFFgf44cVLHNbZ56Lc8656lbqdXIH5Ld4LJj2AUn7l7geAVcCj5rZj/Pi+a0zjwEejo/nA7Nii8k9gSnAPWa2BtgsaVpc54nAvLxlci0njwMWxfN2twBHSBoeq0OPiDHnnHO9VKlHchcB/yC5gcnbgC8TLhBvz3Tgk8BDkupj7GvAxyVNJVQfrgQ+C2BmyyTNAR4htMw8LbasBDgVuIrQ6vPmeIOQRK+VtIJwBDcrrmu9pPOAe+N855rZ+hLK7JxzrodSOMhpZyZpI/BRM7s1YdoRhBaQI7qhfJmqq6uzJUuWZF0M55zrUSTdZ2Z1WZcDSm9d2Q/YNWXarsAuXVMc55xzruuUmuTuJVx4neQUwA93CqxYu5kbl6xixdrNWRfFOef6rFLPyZ1D6G1kMeFC6+cIlxCcSLi04H3dUroe6lt/fIhr7n7m1f9PPGQi587cL8MSOedc31TqdXJ3EFojtgI/JfQk8hNCY5D3mdk/uq2EPcyKtZvbJDiAa+56xo/onHMuA+VcJ3cbcIik1wDDCf1DvtxdBeup6lcld+NZv2ojk8cMSZzmnHOue5Q9MnhMbJ7cUkydMKysuHPOue6TmuQkXQhcYmYN8XExZmZndm3ReqbJY4Zw4iETueautufk/CjOOecqL/U6OUlPAUeb2YOSVpLQz2MeM7PXd0P5MtWZ6+RWrN1M/aqNTJ0wzBOcc65Pqabr5FKP5Mxsz7zHkypSml5k8pghntyccy5j7baulDRQ0q2SDqtAeZxzzrku026SM7NthP4p+3V/cZxzzrmuU2qPJ/PZMfK2c8451yOUegnBLcAP4pA4NwFrKWiIYmZJIxQ455xzmSk1yV0X7z8Sb4UMr850zjlXZUpNcnu2P4tzzjlXXdpNcpJGAqOA5870wRwAACAASURBVMxsdfcXyTnnnOsaqQ1PJA2Jo3I/D9wDPCPpbkmTO7oxSRMk/V3So5KWSTo9xkdIWiBpebwfnrfMWZJWSHpc0oy8+IGSHorTLpGkGB8g6YYYXyxpUt4yJ8VtLJd0Ukefh3MuWeOWJh5ctZHGLU1ZF8U5oHjrym8D7we+BXwQ+DwwDriyE9trAb5sZm8GpgGnSdob+Cqw0MymAAvj/8Rps4B9gCOBSyXlzv1dRhjLbkq8HRnjJxM6j54MXARcENc1AjgbOBg4CDg7P5k65zpnXv1qpl+wiBOuWMz0CxYxv94rflz2iiW5o4BvmNn5ZvZXM7sM+ATwDklDO7IxM1tjZvfHx5uBRwmJcyZhnDrife5yhZnA9WbWZGZPASuAg2Irz93M7C4L/ZJdU7BMbl03AofHo7wZwAIzW29mG4AF7EiMzrlOaNzSxJlzl7KtuZXNTS1sa27ljLlL/YjOZa5YknsdYUTwfIsBxWmdEqsR3xLXOcbM1kBIhMDucbZxwKq8xRpibFx8XBhvs4yZtQCbgJFF1lVYrlMkLZG0ZN26dR1/gs71IQ0btlJb0/brpLamhoYNWzMqkXNBsSTXD2guiG3Pm9ZhkgYDc4EvmtmLxWZNiFmReEeX2REwu9zM6sysbvTo0UWK5pzLGT98EM2trW1iza2tjB8+KKMSORe017rye5LW5/2fSxQXStqQFzcz+1gpG5RUS0hwvzaz38fwWkljzWxNrIp8PsYbgAl5i48Hno3x8Qnx/GUaJPUHhgLrY/ywgmVuK6XMHdG4pYmGDVsZP3wQIwcP6K7NOFcVRg4ewIXH7s8Zc5dSW1NDc2srFx67v+/7LnPFktwdhCO2wsOZ2+NyZR/mxHNjVwKPmtmP8ybNB04Cvh/v5+XFfyPpx8AehAYm95jZdkmbJU0jVHeeCPy0YF13AccBi8zMJN0CfDevsckRwFnlPodSzKtfzZkFH/ajpu5UM+pcr3LU1HFMnzzKf9y5qlJsqJ3DumF704FPAg9Jqo+xrxGS2xxJJwPPAMfHMiyLlzE8QmiZeZqZ5apMTwWuAgYBN8cbhCR6raQVhCO4WXFd6yWdx47zjOeaWf5RapfIPwG/jVB9c8bcpUyfPMo/9K7XGzl4gO/nrqqU2uNJlzCzO0k+NwZweMoy5wPnJ8SXAPsmxLcRk2TCtNnA7FLL2xG5E/C5BAc7TsD7h9855yqr1FEIXIn8BLxzzlUPT3JdLHcCfmBtDUMG9GdgbY2fgO/BVqzdzI1LVrFi7easi+Kc64CKVlf2FX4Cvnf41h8f4pq7n3n1/xMPmci5M/fLsETOuXL5kVw3GTl4AAdMGOYJrodasXZzmwQHcM1dz/gRnXM9TLtJTtJASU2SfGRw12fUr9pYVtw5V53aTXKxteLzhCb8zvUJUycMKyvunKtOpVZX/gL4QuytxLkuV20NPCaPGcKJh0xsEzvxkIlMHjMkoxI55zqi1IYnwwjXpK2UtBBYS9t+H83Mzuzqwrm+oVobeJw7cz9OnDaJ+lUbmTphmCc453qgUpPcsUBuzIx3Jkw3wJOcK1taA48Tp02qiqQyecyQqiiHc65jSkpyZrZndxfE9U3FGnh4cnHOdZZfQuAy5Q08nHPdqeSLweMIAtOBvYCBhdPN7NIuLFePt2LtZj+XU4JcA49r7mp7Ts5fM+dcV5DZTuOG7jyTNAZYCOxN2wFIX13YzDo1kGo1qqursyVLlpS9XLU2pKhm/qPAud5D0n1mVpd1OaD06sofAZsIg5EKOBiYBHwTWE44unN4TxkdNXnMEI6rm+AJzjnXpUpNcu8iJLo18X+Z2TNm9l3gOsCrKiPvKcP1ZY1bmnhw1UYatzS1P7NzFVDOdXLrzKxV0ovA7nnT/oVfPvAqb0jh+qp59as5c+5SamtqaG5t5cJj9+eoqeOyLpbr40o9knsKGBsfLwP+X960DxNG4G6XpNmSnpf0cF7sHEmrJdXH2wfypp0laYWkxyXNyIsfKOmhOO2S2CgGSQMk3RDjiyVNylvmJEnL4+2kEp932bynDNcXNW5p4sy5S9nW3Mrmpha2NbdyxtylfkTnMlfqkdxfgCOAOcB3gHmSGoBmYCKlH8ldBfwMuKYgfpGZ/TA/IGlvYBawD7AH8DdJe5nZduAy4BTgbuAm4EjgZuBkYIOZTZY0C7gA+JikEcDZQB2hscx9kuab2YYSy10W7ynD9TUNG7ZSW1PDNnYMGFxbU0PDhq0+EofLVKkXg5+V9/hmSW8HjgEGAQvM7OYS13NH/tFVO2YC15tZE/CUpBXAQZJWAruZ2V0Akq4BjiYkuZnAOXH5G4GfxaO8GbGc6+MyCwiJ8bcllqVs3lOG60vGDx9Ec2trm1hzayvjhw/KqETOBR26GNzMlpjZ183sf0pNcO34vKSlsTpzeIyNA1blzdMQY+Pi48J4m2XMrIXQInRkkXXtRNIpkpZIWrJu3brOPSvn+oiRgwdw4bH7M7C2hiED+jOwtoYLj93fj+Jc5lKP5CSNBC4HLjezW1LmmUGoNjzVzJ7vYBkuA84jVCOeR2jF+Wl2XIuXz4rE6eAybYNmlxOeN3V1de1fROicA+CoqeOYPnkUDRu2Mn74IE9wrioUO5L7IvB64NYi89wK7Al8uaMFMLO1ZrbdzFqBXwIHxUkNhOvycsYDz8b4+IR4m2Uk9QeGEhrFpK3LVQlvet47jBw8gAMmDPME56pGsST3UeD/rEiXKHHaLwjnwjpE0ti8f48Bci0v5wOzYovJPYEpwD1mtgbYLGlaPN92IjAvb5lcy8njgEWxjLcAR0gaHqtDj4gxVwXm1a9m+gWLOOGKxUy/YBHz61dnXSTXQdU2LqBzxRqevA54pIR1PEro/aRdkn4LHAaMiq0zzwYOkzSVUH24EvgsgJktkzQnlqEFOC22rAQ4ldBScxChwUnuvOCVwLWxkcp6QutMzGy9pPOAe+N85+Yaobhs5Tc9z7XMO2PuUqZPHuVHAz2Md2fnqlGxJLcV2K2EdQyO87bLzD6eEL6yyPznA+cnxJcQBnEtjG8Djk9Z12xgdinldJXjTc97h2ofF9D1XcWqK+8HjiphHTPjvM6VzZue9w7enZ2rVsWS3M+Bk4v1DiLpROA/CBd4O1e2am967g1iSuPd2blqlVpdaWa/l/QT4FeSPg/8FXiGcO5sIuEC6zpCbyV/qERhXe9UrU3PvS/G0vm4gK5atTuenKQPEy4neDuQ+/ZpAv4JXGxmf+7WEmaoo+PJuZ6vcUsT0y9YxLbmHVWpA2tr+OeZ76maJFyNljzVyB3LX+DQKaOo23Nk1sVxGamm8eTa7dbLzP4E/Cled5bbaxtjjyLO9UreIKZ88+pXc8aNS+lXI35xx5P84Dg/8nXZK7lbLzNriRdur/UE53o7bxBTnsYtTfzv7x6kqaWVl1/ZTlNLK1/+3YN+LtNlrkN9V7r2eYOF8vzi78t5/0/u4Bd/X551UYAdDWJ26ScG9K9hl37yBjFFLHt2E83b2576aN5uLHt2U0Ylci4odagdVwZvsFCeN3/jJra2hC/IR9ds5uKFy3n0Ox9oZ6nut2Tlel7ZbuS6OV3y9PqqeB+rc/9K6h62WNy5yvAjuS7mg0eW5xd/X/5qgsvZ2mKZH9GlXdycdXdV1bp/vaY2+askLe5cpfge2MVyDRby5RosuJ39cemasuKVUq0XN1fr/vVwSrVkWty5SvEk18W8wUJ5jt5/bFnxSpk08jVlxSulWvevAf37lRV3rlI8yXWxXIOFAf1reM0u/RjQv7p68Kg2n333FAb1b3veZlB/8dl3T8moREFt/370Kzid1E8hnqVq7SHm7ieSBxhOiztXKd7wpBtY7q+JlLFZXZ7vH3cAp19f/+r/Fxx3QIalCcYPH7TTO2cxnrVq7CHm/lXJ1ZJpcecqxY/kuliuYUBTi/Fy83aaWqwqGgZUq8YtTXx5Tn2b2P/Mqc/89drw0iu0FmS5VgvxalBtg5O2bm8tK+5cpXiS62LV2jCgWi179kVaCr4HW1pDPEvV2vCkWj23OTn5p8Wdq5SKJjlJsyU9L+nhvNgISQskLY/3w/OmnSVphaTHJc3Iix8o6aE47ZI4QjhxFPEbYnyxpEl5y5wUt7G82MgKnVWtDQOqV1p1brbVvN6rfnnGDRtYVty5Sqn0kdxVwJEFsa8CC81sCrAw/o+kvQkje+8Tl7lUUu6s/2XAKcCUeMut82Rgg5lNBi4CLojrGkEYhfxg4CDg7Pxk2pWqtWFAtdpnj6HUFrTwqO0n9tljaEYlch0xdmhyMkuLO1cpFW14YmZ35B9dRTOBw+Ljq4HbgDNj/HozawKekrQCOEjSSmA3M7sLQNI1wNHAzXGZc+K6bgR+Fo/yZgALzGx9XGYBITH+tqufI1Rnw4BqNXLwAH50/AF85calr8Z+cFz2PwqKVVf68DFJvMcTV52qoXXlGDNbA2BmayTtHuPjgLvz5muIseb4uDCeW2ZVXFeLpE2EkRNejScs04akUwhHiUycOLHDT2rk4AGZf1H3FLnWqP1Uw3arjoYKXl1ZnuGvSf4qSYs7VynV3PAk6SegFYl3dJm2QbPLzazOzOpGjx5dUkFdx1Vra9Thu+5SVryve3TNlrLizlVKNSS5tZLGAsT752O8AZiQN9944NkYH58Qb7NMHP9uKLC+yLpcxho2bMUK2upbq2XeGvXWZc+VFe/r+qVcI58Wd65SqiHJzQdyrR1PAublxWfFFpN7EhqY3BOrNjdLmhbPt51YsExuXccBiywMfX4LcISk4bHByREx1m2qbSiUarXrLv1oKhiipWm7sesu2X47Lnl6fVnxvu4db0iu9UiLO1cpFa0wl/RbQiOTUZIaCC0evw/MkXQy8AxwPICZLZM0B3gEaAFOM7PtcVWnElpqDiI0OLk5xq8Ero2NVNYTWmdiZuslnQfcG+c7N9cIpTtU51Ao1emx55J79X/suc2ZNvBoLbwSvJ14X7fupW1lxZ2rlEq3rvx4yqTDU+Y/Hzg/Ib4E2Dchvo2YJBOmzQZml1zYDsofCmUboRHFGXOXMn3yKG+IkuCFLclfgmnxSikcALS9eF/3eMq5t7S4c5VSDdWVvYr3eFKefVOuh0uLV8rrUkYbSIv3dYNqky8VSIs7Vyme5LqY93hSnudeTD5nmRavlCm7Dy4r3tdt3NpSVty5SvEk18W8x5PyVGt15Zwlq8qK93XrX0r+UZIWd65S/ErNbuA9npRuwvDk6r+0eKUsW53cQXRavK8b0L+GrYU9bce4c1nyPbCbVNtQKNWqWOvKLDWnDBGTFu/r9h6b3BNMWty5SvEk5zJWnaMQUJOy/bR4HzftDSPKijtXKZ7kXKbWbExudZoWr5S0LjSrpGvNqtOUUFVZLO5cpXiSc5m69ZG1ZcUrpXl7efG+bn3KiOlpcecqxZOcy9Sg2uTuu9LildIv5fKutHhfV+0Xz69Yu5kbl6xixdpsz/W6yvPWlS5T+40fxtMbdu70eL/xGTdY8OHRyvKm1yZ3wZYWr6Rv/fEhrrn7mVf/P/GQiZw7c78MS+QqyY/kXKb222O3suKVstvA2rLifd1er01+v9LilbJi7eY2CQ7gmrue8SO6PsSTnMvUnSsay4pXym6DUpJcSryv22PowLLilVJshHfXN3iSc5l66ZXkbp/S4pXSsOHlsuJ93UuvbKemoCq3RiGeJR/h3XmSc5mq1o6Qm1K+m9Pifd0Lm7dROApRq4V4lobvustOp1GFj/Del3iSc5manNLhcVrcVae7n0oenjEtXikNG7ayS0GT2F36yUcF6UM8yXUTb7JcmoP3HFlW3FWntJHcsx7hvVpHnneVUzWXEEhaCWwGtgMtZlYnaQRwAzAJWAl81Mw2xPnPAk6O83/BzG6J8QPZMWr4TcDpZmaSBgDXAAcCjcDHzGxldzwXb7JcumodaseV58WtzWXFK+WlV7YzsLaGbc07el4ZWFuT+blCVznVdiT3bjObamZ18f+vAgvNbAqwMP6PpL2BWcA+wJHApZJyP80uA04BpsTbkTF+MrDBzCYDFwEXdMcT8CbL5Xm68aWy4q46rUs595YWr5S0cRx9fMe+o9qSXKGZwNXx8dXA0Xnx682sycyeAlYAB0kaC+xmZneZmRGO3I5OWNeNwOGSuvzS3luW7Xxhc7F4X1etDU9ceUYPSb5UIC1eKT6+o6ua6kpCt/O3SjLgF2Z2OTDGzNYAmNkaSbvHeccBd+ct2xBjzfFxYTy3zKq4rhZJm4CRwAv5hZB0CuFIkIkTJ5b9JF5OafqeFu/rDnnDqLLirjqNHZZ8ZJQWryQf37Fvq6Yjuelm9lbg/cBpkg4tMm/SEZgViRdbpm3A7HIzqzOzutGjR7dX5p1U6yCg1eof/36+rLirTk+t21JWvNKeWreFhY+urZryuMqpmiM5M3s23j8v6Q/AQcBaSWPjUdxYIPfN1wBMyFt8PPBsjI9PiOcv0yCpPzAU6PL2zXukJLO0eF933d1Pp8aPfuuExGmu+jyzPrlJflq8kk644u5Xe9C5ZNEK3jl5JNd+ZlrGpapujVuaes2Rb1UcyUnaVdKQ3GPgCOBhYD5wUpztJGBefDwfmCVpgKQ9CQ1M7olVm5slTYvn204sWCa3ruOARfG8XZfaZ4/d6F/wqvavCXG3s5bW5PHG0uKuOm1tTm5FmRavlCVPNe7URdw/VjSy5Klsu42rZvPqVzPtu3/juMv+xbTv/o359auzLlKnVMuR3BjgD7EdSH/gN2b2V0n3AnMknQw8AxwPYGbLJM0BHgFagNPMLNcm+FR2XEJwc7wBXAlcK2kF4QhuVnc8kZGDB1BbI1ryun+orVHV/Bqqtl9oTc3JvzPS4q46PbspuRVlWrxS7lj+Qmq8zq/F3EnjliZOv74+/hc+g1+4vp7pk0dVxfdFR1RFkjOzJ4EDEuKNwOEpy5wPnJ8QXwLsmxDfRkyS3em6fz3F1pa2X9BbW4zr/vUUJ7x9z+7efFHz6ldzxo1LEWH3/cFx+3PU1HHtLdatXtmefMSWFnfVaffBA1mzaecBUncfnG3rykOnjOKSRSsS425nt6a0Ar912XN8/ODXVbg0XaMqqit7kxuWNJQVr5TGLU38z5wHaWppZVtLK00trXxpzoM0bsn2ousxKU3M0+KuOjW+lLwfpcUrpW7PkbxzctsjtndOHulHcSmefCG5YU5avCfwJNfFXjMg+SVNi1fKXU80sr2gB93trcZdT2R7bmLYrsmVCWlxV53WbEpOZmnxSjqubgK1NeG0QW0NHF/nDZrSzNj7tWXFewJPcl2stib5JU2LV8qjazaVFa+UVSmt79LiLmjc0sSDqzZmfiSeMyilU/+0eKU0bmnizLlLaW6F5lajuRXOmLu0al63atMbj3z953IXmzJmCHc+sfOVCVPGDMmgNDusf2nn8yXF4pXyzPrk7rvS4m7HudV+NWJ7q1XFudWtKbtRWrxSGjZspbamhm3sOMdbW1NDw4atPbYhRXe79jPTWPJUI3csf4FDp4zq0QkOPMl1uQPGDy0rXin9Uo4k0+KV8nJTcgOTtHhf17ilif/93YM05/Ws/+XfPZh567ftKY1h0+KVMn74IJoLLkdpbm31vivbsefowdT279crXievruxiT76QPHJ0WrxS3v3G5N5b0uKVUnhNYXvxvm7Zs5vaJDiA5u3GsmezrXYenXIONS1eKSMHD2DYoLZlGD6ovx/FFTGvfjWHfG8hH/3FXRzyvYU9/jo5/yrpYlubUi6KTYlXypMvJFf/pcUrZWvKiCdp8b5uwbK1ZcUrpX/Kr5K0eKUsfOQ5nnuxbZ3pmhdfYeEj3mF6ksYtTXzphnpe2W40tbTyynbjizfU9+hzmJ7kulj96hfLilfK7Y8n9wWZFnfVadFjycksLV4pSdfIFYtXyq8XP1NWvK+764kXKGiETauFeE/lSa6L7do/efSetHilNLUkHxqlxV112vJKco1AWryvW54yjmNavK97ujH5tEpavCfwJNfF7n1mY1nxStn4cvJQP2lxV522p7TkSIv3dc+nDNqaFu/rWlJ6GkqL9wSe5LrYKy0p3VSlxCvFj+R6h22vJCeztHhfl1Z/km29SvV6fkty9XJavCfwJNfFdulXXrxSJo7ctay4q04Ddkn+yKbF+7ptKb/h0uJ93ZghyVfvp8V7Av9kdLGtKadG0uKVsiHlou+0uKtOH9h3bFlx58qxW0oXNWnxnsCTXBerTTliS4tXyuqNyZcKpMVddarWzgZc7/COycmjM6TFewJPcl2sWs8BjB2a3HNBWtxVp0tvf6KsuHPlmDxmSGLflZMz7pawMzzJdbG9xiSPAJ4Wr5TPHvqGsuKuOq1N6dU/Le5cORq3NHHv0xvaxO59eoNfDN6TSDpS0uOSVkj6alev/5R3vb6seKWMSBm8Mi3uqlP/lGrvtLhz5ch1aJ0v16F1T9WnkpykfsDPgfcDewMfl7R3V27jkDeMoqagbrJGIZ6ttCbm2TY9H7xLckVuWryvOzilR/i0uHPl6I0dWvepJAccBKwwsyfN7BXgemBmV25g5OABXPyxqezSTwzoX8Mu/cTFH5uaeYew++wxlNp+bRNHbT+xzx7ZNlh4X8pgjGnxvu5bH96nrHhf952jkn/DpsX7upGDB3DhsfszsLaGIQP6M7C2hguP3T/z76/O6GtD7YwDVuX93wAcnD+DpFOAUwAmTpzYoY0cNXUc0yePomHDVsYPH1QVO8jIwQP40fEH8JWCcciyLttp757CH+rXJMbdziaPGcKJh0zkmrt29L144iETM28YcPq7X89P/v5kYjxLJ7x9T35w6+NsyrswbujAfpzw9j0zLFV1q8bvr86QWd/pKUHS8cAMM/tM/P+TwEFm9t9J89fV1dmSJUsqWcRu17ilqep23m/Ne2inL+1zZ+6XYYnC63Tgd/62U/y+b7y3Kl63FWs3U79qI1MnDMs8weVMOesvNOd9ndQKln/vg9kVKM91/3qKeUvXMHP/sZ7gKkDSfWZWl3U5oO8luUOAc8xsRvz/LAAz+17S/L0xyVWravzSnl+/mi9cX//q/5fMmpr5CNzV7qJbHmXe0ueYuf9r+dKMN2ddHJcRT3IZkdQf+DdwOLAauBf4hJktS5rfk5yrxiNf56pdNSW5PnVOzsxaJH0euAXoB8xOS3DOQTiX6cnNuZ6rTyU5ADO7Cbgp63I455zrfn3tEgLnnHN9iCc555xzvZYnOeecc72WJznnnHO9Vp+6hKBcktYBT3diFaOAF7qoOF3Jy1UeL1d5vFzl6Y3lep2Zje7KwnSUJ7luJGlJtVwrks/LVR4vV3m8XOXxcnUvr650zjnXa3mSc84512t5kutel2ddgBRervJ4ucrj5SqPl6sb+Tk555xzvZYfyTnnnOu1PMk555zrtTzJdTFJEyT9XdKjkpZJOj3rMgFIGijpHkkPxnJ9O+sy5ZPUT9IDkv6cdVlyJK2U9JCkeklVM+aSpGGSbpT0WNzPDsm6TACS3hhfq9ztRUlfrIJyfSnu8w9L+q2kgVmXCUDS6bFMy7J+nSTNlvS8pIfzYiMkLZC0PN4Pz7KMHeVJruu1AF82szcD04DTJO2dcZkAmoD3mNkBwFTgSEnTMi5TvtOBR7MuRIJ3m9nUKrte6CfAX83sTcABVMnrZmaPx9dqKnAg8DLwhyzLJGkc8AWgzsz2JQyxNSvLMgFI2hf4T+Agwnv4IUlTMizSVcCRBbGvAgvNbAqwMP7f43iS62JmtsbM7o+PNxO+gDIfTtqCLfHf2nirilZHksYDHwSuyLos1U7SbsChwJUAZvaKmW3MtlSJDgeeMLPO9BjUVfoDg+Kgya8Bns24PABvBu42s5fNrAW4HTgmq8KY2R3A+oLwTODq+Phq4OiKFqqLeJLrRpImAW8BFmdbkiBWCdYDzwMLzKwqygVcDJwBtGZdkAIG3CrpPkmnZF2Y6PXAOuBXsXr3Ckm7Zl2oBLOA32ZdCDNbDfwQeAZYA2wys1uzLRUADwOHShop6TXAB4AJGZep0BgzWwPhxzuwe8bl6RBPct1E0mBgLvBFM3sx6/IAmNn2WJU0HjgoVplkStKHgOfN7L6sy5Jgupm9FXg/odr50KwLRDgqeStwmZm9BXiJKqtGkrQLcBTwuyooy3DCEcmewB7ArpJOyLZUYGaPAhcAC4C/Ag8STnW4LuZJrhtIqiUkuF+b2e+zLk+hWL11GzvXwWdhOnCUpJXA9cB7JF2XbZECM3s23j9POLd0ULYlAqABaMg7Cr+RkPSqyfuB+81sbdYFAd4LPGVm68ysGfg98PaMywSAmV1pZm81s0MJVYXLsy5TgbWSxgLE++czLk+HeJLrYpJEOF/yqJn9OOvy5EgaLWlYfDyI8OF/LNtSgZmdZWbjzWwSoYprkZll/ktb0q6ShuQeA0cQqpgyZWbPAaskvTGGDgceybBIST5OFVRVRs8A0yS9Jn42D6dKGupI2j3eTwQ+QvW8ZjnzgZPi45OAeRmWpcP6Z12AXmg68EngoXj+C+BrZnZThmUCGAtcLakf4cfNHDOrmub6VWgM8IfwvUh/4Ddm9tdsi/Sq/wZ+HasFnwT+I+PyvCqeX3of8NmsywJgZosl3QjcT6gOfIDq6a5qrqSRQDNwmpltyKogkn4LHAaMktQAnA18H5gj6WTCj4XjsypfZ3i3Xs4553otr650zjnXa3mSc84512t5knPOOddreZJzzjnXa3mSc84512t5knOuC0j6iKRFkjZKapL0b0nfkTSqG7e5q6TzJD0uaauktZJuj02+u2N7Z0g6rDvW7Vx38UsInOskST8Cvgj8inDB7IvA3sDngCfNrFs63pX0V0LfqN8hXKi+O6Hz5tea2bHdsL0XgJ+Z2TldvW7nuotfDO5cJ0j6MPA/wMlmNjtv0u2S6qSObAAAAy5JREFULif0lNKZ9Q8ys60J8SnADOCjZpbfR+QNsWcP5xxeXelcZ32J0E/j7MIJsUPsm3P/S/p+HIR1i6QGSb+W9Nr8ZeJArT+S9M3Y80Ra597D4v1zCdttUz0jaaKk6yWtl/SypFvyugUrqWyxb9GRwNmSLN4OK/bCOFcNPMk510GxI+63E3qRL8XuwHcJY+d9kTBszqLY1Vq+TwDvAv4L+FjKuh4njEBwsaQj0ka7ljQCuBN4I6H69KPArsDfYh+mpZbtGGAToV/WQ+Lt/tKetnPZ8epK5zpuJDCA0K9fu8zs07nHMXncRRhVYDpwR8HsHzKzbUXW9aKk/wR+CdwCNEu6G7gWuCLvaO5LhKQ21czWx23/E1gJfBr4eSllM7MHJLUQRkC4u5Tn61w18CM55zqvpNZbkt4v6V+SNhE6C26Ik/YqmHVhsQT36kbNfgu8jpCsro/ruRz4Td5s7yWMWfaipP5xdOzNwH1AXQfK5lyP4knOuY5rBJqAie3NKOlthKFLGgijVBwCTIuTC6saSx6HzcwazexXZnYiYWTpXwGzJB0QZxlFqPJsLri9O85fbtmc61G8utK5DjKz5lj1NwP4RjuzHwOsAz6Wq0qU9Lq0VXeiPBcRht55E2G06fWEBHZewiKbO1A253oUT3LOdc7FwHxJJ5nZ1fkTJNUAR8Rx6AYBzQUtH/9fRzcaB3RtSbi8YEq8zx0NLiQ0NlmWdClCVGrZXsGP7FwP40nOuU4wsz9J+jFwpaTphIvBtxCOpD5HaODxV8J5sS9Kuhj4E6FVZmdGQH8jIbnOBv4FvAxMBb4O1BNaVAL8OG5nkaSfAqsJA8K+C7gzntcrtWyPAR+MF6FvAR43s80J8zlXNfycnHOdZGZfJpz3mkJo9LEA+DLhKOrUOM9NwJnAsYTqw3cBH+rEZp8AriCMwn0tcBMhqc4GDjezlrjdFwjn1x4DLgJuBS4EhgJLyyzbVwiXLfwFuBc4sBPld64ivFsv55xzvZYfyTnnnOu1PMk555zrtTzJOeec67U8yTnnnOu1PMk555zrtTzJOeec67U8yTnnnOu1PMk555zrtf4/G8+Q3EUV3E4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.plot(kind=\"scatter\", x=\"Car_Seat\", y=\"Car_Price\")\n",
    "plt.title('Car Price for different Number of Seats', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Seat', fontsize = 15) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86261</td>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005 Nissan Tiida Axis</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Tiida</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79395</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009 Toyota Wish</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Wish</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1800</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85473</td>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88342</td>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>71719</td>\n",
       "      <td>9400</td>\n",
       "      <td>1</td>\n",
       "      <td>2010 Nissan X-Trail 20XT</td>\n",
       "      <td>2010</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>X-Trail</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>125384</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_No  Car_Price  Car_ORC  \\\n",
       "0  86261       3997        0   \n",
       "1  79395       7997        0   \n",
       "2  85473       9940        1   \n",
       "3  88342       8940        1   \n",
       "5  71719       9400        1   \n",
       "\n",
       "                                           Car_Title  Car_Year   Car_Brand  \\\n",
       "0                             2005 Nissan Tiida Axis      2005      Nissan   \n",
       "1                                   2009 Toyota Wish      2009      Toyota   \n",
       "2  2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...      2007  Mitsubishi   \n",
       "3  2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...      2006  Mitsubishi   \n",
       "5                           2010 Nissan X-Trail 20XT      2010      Nissan   \n",
       "\n",
       "   Car_Model Car_Location  Car_Mileage(km)       Car_Type   Car_Gear  \\\n",
       "0      Tiida      Dunedin           138150      Hatchback  Automatic   \n",
       "1       Wish      Dunedin           100300  Station Wagon  Automatic   \n",
       "2  OUTLANDER     Auckland           102425         RV/SUV  Automatic   \n",
       "3  OUTLANDER     Auckland           109425         RV/SUV  Automatic   \n",
       "5    X-Trail     Auckland           125384         RV/SUV  Automatic   \n",
       "\n",
       "   Car_Seat  Car_Fuel(cc)  Car_Door_Num Car_Fuel_Type Car_Color  \n",
       "0       5.0          1500           5.0        Petrol    Silver  \n",
       "1       7.0          1800           5.0        Petrol     White  \n",
       "2       5.0          2400           5.0        Petrol    Silver  \n",
       "3       5.0          2400           5.0        Petrol     Black  \n",
       "5       5.0          2000           5.0        Petrol     Black  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.plot(kind=\"scatter\", x=\"Car_Year\", y=\"Car_Price\")\n",
    "plt.title('Car Price for different year of car', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Year', fontsize = 15) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete outlier: Car year earlier than 1985. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door_fuel.drop(Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Year'] < 1990)].index, axis=0, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.plot(kind=\"scatter\", x=\"Car_Year\", y=\"Car_Price\")\n",
    "plt.title('Car Price for different year of car', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Year', fontsize = 15) \n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#box plot Districts/Total Price\n",
    "f, ax = plt.subplots(figsize=(15, 10))\n",
    "fig = sns.boxplot(x='Car_Type', y=\"Car_Price\", data=Car_data_target_seat_door_fuel)\n",
    "plt.title('Car Price for Different Car Type', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Type', fontsize = 15) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x=\"Car_Brand\", y=\"Car_Price\", \n",
    "              data=Car_data_target_seat_door_fuel, jitter=True,\n",
    "              palette=\"Set2\",  # 设置调色盘\n",
    "              dodge=True,  # 是否拆分\n",
    "             )\n",
    "plt.title('Car Price for Different Car Brand', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Brand', fontsize = 15) \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10065 entries, 0 to 13438\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Car_No           10065 non-null  object \n",
      " 1   Car_Price        10065 non-null  int32  \n",
      " 2   Car_ORC          10065 non-null  int32  \n",
      " 3   Car_Title        10065 non-null  object \n",
      " 4   Car_Year         10065 non-null  int32  \n",
      " 5   Car_Brand        10065 non-null  object \n",
      " 6   Car_Model        10065 non-null  object \n",
      " 7   Car_Location     10065 non-null  object \n",
      " 8   Car_Mileage(km)  10065 non-null  int32  \n",
      " 9   Car_Type         10065 non-null  object \n",
      " 10  Car_Gear         10065 non-null  object \n",
      " 11  Car_Seat         10065 non-null  float64\n",
      " 12  Car_Fuel(cc)     10065 non-null  int32  \n",
      " 13  Car_Door_Num     10065 non-null  float64\n",
      " 14  Car_Fuel_Type    10065 non-null  object \n",
      " 15  Car_Color        10065 non-null  object \n",
      "dtypes: float64(2), int32(5), object(9)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only keep valuable columns \n",
    "\n",
    "Car_data_target_final = Car_data_target_seat_door_fuel.drop(['Car_No', 'Car_Title', 'Car_Model', 'Car_Color'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1800</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9400</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>125384</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Car_Price  Car_ORC  Car_Year   Car_Brand Car_Location  Car_Mileage(km)  \\\n",
       "0       3997        0      2005      Nissan      Dunedin           138150   \n",
       "1       7997        0      2009      Toyota      Dunedin           100300   \n",
       "2       9940        1      2007  Mitsubishi     Auckland           102425   \n",
       "3       8940        1      2006  Mitsubishi     Auckland           109425   \n",
       "5       9400        1      2010      Nissan     Auckland           125384   \n",
       "\n",
       "        Car_Type   Car_Gear  Car_Seat  Car_Fuel(cc)  Car_Door_Num  \\\n",
       "0      Hatchback  Automatic       5.0          1500           5.0   \n",
       "1  Station Wagon  Automatic       7.0          1800           5.0   \n",
       "2         RV/SUV  Automatic       5.0          2400           5.0   \n",
       "3         RV/SUV  Automatic       5.0          2400           5.0   \n",
       "5         RV/SUV  Automatic       5.0          2000           5.0   \n",
       "\n",
       "  Car_Fuel_Type  \n",
       "0        Petrol  \n",
       "1        Petrol  \n",
       "2        Petrol  \n",
       "3        Petrol  \n",
       "5        Petrol  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical features are:  ['Car_Brand', 'Car_Location', 'Car_Type', 'Car_Gear', 'Car_Fuel_Type']\n",
      "Numerical features are:  ['Car_Price', 'Car_ORC', 'Car_Year', 'Car_Mileage(km)', 'Car_Seat', 'Car_Fuel(cc)', 'Car_Door_Num']\n"
     ]
    }
   ],
   "source": [
    "# distinguish features into categorical and numerical features \n",
    "\n",
    "categorical_features = Car_data_target_final.select_dtypes(include = [\"object\"]).columns\n",
    "numerical_features = Car_data_target_final.select_dtypes(exclude = [\"object\"]).columns\n",
    "print('Categorical features are: ', categorical_features.tolist())\n",
    "print('Numerical features are: ',numerical_features.tolist())\n",
    "\n",
    "Car_data_categorical_columns = Car_data_target_final[categorical_features]\n",
    "\n",
    "Car_data_numerical_columns = Car_data_target_final[numerical_features].drop(['Car_Price'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dummy values \n",
    "\n",
    "df_categorical_columns_dummies = pd.get_dummies(Car_data_categorical_columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand_Aston</th>\n",
       "      <th>Car_Brand_Audi</th>\n",
       "      <th>Car_Brand_BMW</th>\n",
       "      <th>Car_Brand_Bentley</th>\n",
       "      <th>Car_Brand_Cadillac</th>\n",
       "      <th>Car_Brand_Chevrolet</th>\n",
       "      <th>Car_Brand_Chrysler</th>\n",
       "      <th>Car_Brand_Citroen</th>\n",
       "      <th>Car_Brand_Daihatsu</th>\n",
       "      <th>Car_Brand_Fiat</th>\n",
       "      <th>...</th>\n",
       "      <th>Car_Type_Ute</th>\n",
       "      <th>Car_Type_Van</th>\n",
       "      <th>Car_Gear_Automatic</th>\n",
       "      <th>Car_Gear_Manual</th>\n",
       "      <th>Car_Gear_Tiptronic</th>\n",
       "      <th>Car_Fuel_Type_Diesel</th>\n",
       "      <th>Car_Fuel_Type_Electric</th>\n",
       "      <th>Car_Fuel_Type_Hybrid</th>\n",
       "      <th>Car_Fuel_Type_LPG</th>\n",
       "      <th>Car_Fuel_Type_Petrol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Car_Brand_Aston  Car_Brand_Audi  Car_Brand_BMW  Car_Brand_Bentley  \\\n",
       "0                0               0              0                  0   \n",
       "1                0               0              0                  0   \n",
       "2                0               0              0                  0   \n",
       "3                0               0              0                  0   \n",
       "5                0               0              0                  0   \n",
       "\n",
       "   Car_Brand_Cadillac  Car_Brand_Chevrolet  Car_Brand_Chrysler  \\\n",
       "0                   0                    0                   0   \n",
       "1                   0                    0                   0   \n",
       "2                   0                    0                   0   \n",
       "3                   0                    0                   0   \n",
       "5                   0                    0                   0   \n",
       "\n",
       "   Car_Brand_Citroen  Car_Brand_Daihatsu  Car_Brand_Fiat  ...  Car_Type_Ute  \\\n",
       "0                  0                   0               0  ...             0   \n",
       "1                  0                   0               0  ...             0   \n",
       "2                  0                   0               0  ...             0   \n",
       "3                  0                   0               0  ...             0   \n",
       "5                  0                   0               0  ...             0   \n",
       "\n",
       "   Car_Type_Van  Car_Gear_Automatic  Car_Gear_Manual  Car_Gear_Tiptronic  \\\n",
       "0             0                   1                0                   0   \n",
       "1             0                   1                0                   0   \n",
       "2             0                   1                0                   0   \n",
       "3             0                   1                0                   0   \n",
       "5             0                   1                0                   0   \n",
       "\n",
       "   Car_Fuel_Type_Diesel  Car_Fuel_Type_Electric  Car_Fuel_Type_Hybrid  \\\n",
       "0                     0                       0                     0   \n",
       "1                     0                       0                     0   \n",
       "2                     0                       0                     0   \n",
       "3                     0                       0                     0   \n",
       "5                     0                       0                     0   \n",
       "\n",
       "   Car_Fuel_Type_LPG  Car_Fuel_Type_Petrol  \n",
       "0                  0                     1  \n",
       "1                  0                     1  \n",
       "2                  0                     1  \n",
       "3                  0                     1  \n",
       "5                  0                     1  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_categorical_columns_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train : (7045, 70)\n",
      "X_test : (3020, 70)\n",
      "y_train : (7045,)\n",
      "y_test : (3020,)\n"
     ]
    }
   ],
   "source": [
    "# Create dataset for ML \n",
    "# Split dataset for the training and testing \n",
    "car_train_len = int(len(Car_data_target_final)*0.7) \n",
    "\n",
    "y_train = Car_data_target_final['Car_Price'][:car_train_len]\n",
    "\n",
    "y_test = Car_data_target_final['Car_Price'][car_train_len:]\n",
    "\n",
    "\n",
    "df_train_num = Car_data_numerical_columns[:car_train_len] \n",
    "df_train_dum = df_categorical_columns_dummies[:car_train_len]\n",
    "X_train = pd.concat([df_train_num, df_train_dum], axis = 1)\n",
    "df_train = pd.concat([X_train, y_train], axis = 1)\n",
    "\n",
    "\n",
    "df_test_num = Car_data_numerical_columns[car_train_len:] \n",
    "df_test_dum = df_categorical_columns_dummies[car_train_len:]\n",
    "X_test = pd.concat([df_test_num, df_test_dum], axis = 1)\n",
    "df_test = pd.concat([X_test, y_test], axis = 1)\n",
    "\n",
    "\n",
    "print(\"X_train : \" + str(X_train.shape))\n",
    "print(\"X_test : \" + str(X_test.shape))\n",
    "print(\"y_train : \" + str(y_train.shape))\n",
    "print(\"y_test : \" + str(y_test.shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Price                1.000000\n",
       "Car_Fuel(cc)             0.417299\n",
       "Car_Year                 0.381062\n",
       "Car_Fuel_Type_Diesel     0.367488\n",
       "Car_Brand_Lamborghini    0.363655\n",
       "                           ...   \n",
       "Car_Brand_Smart          0.001320\n",
       "Car_Brand_Cadillac       0.001038\n",
       "Car_Type_Sedan           0.001023\n",
       "Car_Brand_Renault             NaN\n",
       "Car_Brand_Rolls-Royce         NaN\n",
       "Name: Car_Price, Length: 71, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train_corr = pd.concat([X_train, y_train], axis = 1)\n",
    "corr_value = df_train_corr.corr().abs()\n",
    "sns.heatmap(corr_value,square=True,vmax=1,vmin=-1,center=0.0,cmap='coolwarm')\n",
    "corr_value.sort_values([\"Car_Price\"], ascending = False, inplace = True)\n",
    "corr_value[\"Car_Price\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train Naive Bayes Model\n",
    "nb_model = GaussianNB()\n",
    "nb_model.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7995,  6900,  7995, ..., 14499, 14250,  7995])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predict modl\n",
    "nb_y_pred = nb_model.predict(X_test)\n",
    "nb_y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6490066225165565"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy Score\n",
    "nb_accuracy = accuracy_score(y_test,nb_y_pred) * 100\n",
    "nb_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4900</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7400</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8900</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9900</td>\n",
       "      <td>12390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5800</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3015</th>\n",
       "      <td>14990</td>\n",
       "      <td>10333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3016</th>\n",
       "      <td>7995</td>\n",
       "      <td>6590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3017</th>\n",
       "      <td>14450</td>\n",
       "      <td>14499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3018</th>\n",
       "      <td>12800</td>\n",
       "      <td>14250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3019</th>\n",
       "      <td>4999</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3020 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       test predict\n",
       "0      4900    7995\n",
       "1      7400    6900\n",
       "2      8900    7995\n",
       "3      9900   12390\n",
       "4      5800    7995\n",
       "...     ...     ...\n",
       "3015  14990   10333\n",
       "3016   7995    6590\n",
       "3017  14450   14499\n",
       "3018  12800   14250\n",
       "3019   4999    7995\n",
       "\n",
       "[3020 rows x 2 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compare and visualize the test and predict sample\n",
    "nb_compare_plot = pd.DataFrame({},columns = ['test','predict'])\n",
    "nb_compare_plot\n",
    "\n",
    "for i in range(len(y_test)):\n",
    "    nb_compare_plot.loc[i] = [y_test.values[i],nb_y_pred[i]]\n",
    "    \n",
    "nb_compare_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xg6OPPprPfe5z3Hnnndss22z2OEqSJElSTnbbbTde+9rXst9++/HmN7+Zo446ite85jUA7LDDDpx33nksWbKEz3/+80yaNImpU6dyxhlnAHDsscfy5je/mT333LPpk+NE6dcrNHfu3HTHHXc0O4xtdHd3+1s/Hcq670zWe2ey3juT9d6ZrPfGe+CBB3jxi1/c1Bj6+/ub+luSlVTaLhGxMKU0t9LzHaoqSZIkSarKxFGSJEkt777H1jDnuF9y+zJ/ulvKg4mjJEmSWt5vHnoagKsfeLLJkUjtycRRkiRJklSViaMkSZIkqSoTR0mSJElSVSaOkiRJankJf2JO7e/666/nrW99KwCXXnopp5566ojP7e7u5rvf/W7dXtvEUZIkSZKaaGBgYNzLvP3tb+e4444b8XETR0mSJGmYIJodglTRsmXL2G+//TjmmGN4+ctfznve8x42bNjAnDlzOOmkkzj44IP56U9/ylVXXcVrXvMaXvWqV/He976XdevWAfCrX/2KF73oRRx88MH8/Oc/37Les88+m09+8pMAPPnkk7zzne/kFa94Ba94xSv47W9/y3HHHcfDDz/M/vvvz+c///kJv48pE16DJEmSJLWCK46DJ+6t7zqf9zJ488hDRgEefPBBzjrrLA466CA+8pGPbOkJ7Orq4sYbb+Tpp5/mXe96F1dffTXbb789X/3qVznttNP453/+Z/72b/+Wa6+9ln322Yf3v//9Fdf/6U9/mj//8z/n4osvZmBggHXr1nHqqady3333cffdd9flbdrjKEmSJEk5mj17NgcddBAARx99NDfeeCPAlkTwlltu4f777+eggw5i//33Z/78+fzhD39g8eLF7L333uy7775EBEcffXTF9V977bV8/OMfB2Dy5MnsvPPOdX8P9jhKkiSp5Tk5jsZklJ7BvERExf+33357AFJKvOlNb+L888/f6nl33333Nss2iz2OkiRJahte66gieuSRR7j55psBOP/88zn44IO3evzAAw/kpptuYsmSJQBs2LCB3//+97zoRS9i6dKlPPzww1uWreSQQw7hjDPOAEoT7axdu5Ydd9yRnp6eur0HE0dJkiS1DXseVUQvfvGLmT9/Pi9/+ctZtWrVlmGlQ/bYYw/OPvtsPvCBD/Dyl7+cAw88kMWLF9PV1cWZZ57JW97yFg4++GBe+MIXVlz/t7/9ba677jpe9rKX8epXv5pFixax2267cdBBB7Hffvs5OY4kSZIE9jSq2CZNmsT//M//bFW2bNmyrf5/4xvfyO23377NsocffjiLFy/epvzDH/4wH/7whwF47nOfyyWXXLLNc3784x/XHvQw9jhKkiRJkqoycZQkSVLLc4iqimrOnDl1+0mMZjJxlCRJUttwyKqUDxNHSZIkSW0tJXuky9WyPUwcJUmS1DYcsqrhurq6eOaZZ0weMyklnnnmGbq6usa1nLOqSpIkSWpbs2bNYvny5Tz11FNNi2FwcJBJk4rTZ9fV1cWsWbPGtYyJoyRJktqG1zhquKlTp7L33ns3NYbu7m5mzpzZ1BgmqjhpryRJkiSpkEwcJUmSJElVmThKkiRJkqoycZQkSZIkVWXiKEmSJEmqysRRkiRJklSViaMkSZIkqSoTR0mSJElSVSaOkiRJankpNTuC6u56ZDXv+97NbOofbHYoUk1MHCVJkqScHf/ze7lt6SqWPrOx2aFINTFxlCRJUsuLaHYEUnszcZQkSZIkVWXiKEmSJEmqysRRkiRJLa/ok+NIrc7EUZIkSW3Dax2lfJg4SpIkqW3Y8yjlw8RRkiRJLc+eRilfJo6SJElqefY0SvkycZQkSVLbsOdRyoeJoyRJkiSpKhNHSZIkSVJVJo6SJElqG17rKOXDxFGSJEmSVFVuiWNEdEXEbRFxT0QsiogTs/JdI2JBRDyU3e5StszxEbEkIh6MiMPKyl8dEfdmj50eUbrsOSKmRcQFWfmtETGnbJljstd4KCKOyet9SpIkqTicHEfKR549jn3AG1NKrwD2Bw6PiAOB44BrUkr7Atdk/xMRLwGOBF4KHA58NyImZ+s6AzgW2Df7Ozwr/yiwOqW0D/BN4KvZunYFTgD+DDgAOKE8QZUkSZIkjV1uiWMqWZf9OzX7S8ARwPysfD7wjuz+EcBPUkp9KaWlwBLggIjYE9gppXRzSikB5wxbZmhdFwGHZL2RhwELUkqrUkqrgQU8m2xKkiRJksZhSp4rz3oMFwL7AP+dUro1Ip6bUloBkFJaERHPyZ6+F3BL2eLLs7LN2f3h5UPLPJqtqz8i1gC7lZdXWKY8vmMp9WQye/Zsuru7J/Bu89HT09PsENQk1n1nst47k/Xemaz3+urt7QWgr7e3kMd0gwMDAGzYsL6Q8Slf7fB9zzVxTCkNAPtHxEzg4ojYr8rTK41IT1XKa12mPL4zgTMB5s6dm2bOnFklvOYpalzKn3Xfmaz3zmS9dybrvX66up4GYFpXVyG366TJpSuwZszYvpDxKX+tXu8NmVU1pdQNXE9puOiT2fBTstuV2dOWA7PLFpsFPJ6Vz6pQvtUyETEF2BlYVWVdkiRJkqRxynNW1T2ynkYiYjrwF8Bi4FJgaJbTY4BLsvuXAkdmM6XuTWkSnNuyYa09EXFgdv3ih4YtM7Su9wDXZtdBXgkcGhG7ZJPiHJqVSZIkSZLGKc+hqnsC87PrHCcBF6aULouIm4ELI+KjwCPAewFSSosi4kLgfqAf+EQ21BXg48DZwHTgiuwP4Czg3IhYQqmn8chsXasi4mTg9ux5J6WUVuX4XiVJkiSpbeWWOKaUfge8skL5M8AhIyxzCnBKhfI7gG2uj0wp9ZIlnhUemwfMG1/UkiRJkqThGnKNoyRJkiSpdZk4SpIkSZKqMnGUJEmSJFVl4ihJkiRJqsrEUZIkSZJUlYmjJEmSJKkqE0epAAYHE1cteoKUUrNDkSSppUWzA5DalImjVAA/uu0Rjj13IT9duLzZoUiS1NI8BSvlw8RRKoAn1mwEYOXa3iZHIkmSJG3LxFGSJEmSVJWJoyRJktqG1zhK+TBxlCRJkiRVZeIoSZIkSarKxFGSJEltw1lVpXyYOEqSJEmSqjJxlCRJUttwchwpHyaOUgEkx9VIkiSpwEwcJUmSJElVmThKBRCOq5EkSVKBmThKkiRJkqoycZQkSZIaxFFGalUmjlIBODmOJEmdwTZfrcrEUZIkSZJUlYmjVAAOW5EkSVKRmThKkiSp5SXHgEq5MnGUJEmSJFVl4igVgCdJJUmamPC6DylXJo6SJEmSpKpMHKUC8CSpJEmSiszEUZIkSS3PyXGkfJk4SpIkqW04ikfKh4mjJEmS2kbROx5NbNWqTBwlSZLU8lplVtWiJ7bSSEwcJUmS1PK8xlHKl4mjJEmS2kaLdDxKLcfEUZIkSZJUlYmjJEmS1CD2iKpVmThKkiSpbRT9UseixyeNxMRRkiRJklSViaMkSZLahkNBpXyYOEoF4LAVSZIkFZmJoyRJkiSpKhNHqQAcViNJUmewzVerMnGUJEmSGsTLU9SqTBwlSZIkSVWZOEoF4NlHSZIkFZmJoyRJkiSpKhNHqQC8UF6SpM5gm69WZeIoSZKkltcql320SpzScLkljhExOyKui4gHImJRRPxDVv6ViHgsIu7O/v6ybJnjI2JJRDwYEYeVlb86Iu7NHjs9onSuJiKmRcQFWfmtETGnbJljIuKh7O+YvN6nJEmSJLW7KTmuux/4bErpzojYEVgYEQuyx76ZUvp6+ZMj4iXAkcBLgecDV0fEH6eUBoAzgGOBW4DLgcOBK4CPAqtTSvtExJHAV4H3R8SuwAnAXCBlr31pSml1ju9XqplnHyVJmhiHgEr5yq3HMaW0IqV0Z3a/B3gA2KvKIkcAP0kp9aWUlgJLgAMiYk9gp5TSzSmlBJwDvKNsmfnZ/YuAQ7LeyMOABSmlVVmyuIBSsilJkiRJGqeGXOOYDSF9JXBrVvTJiPhdRMyLiF2ysr2AR8sWW56V7ZXdH16+1TIppX5gDbBblXVJheRZUkmSJqZVRu/Y5qtV5TlUFYCI2AH4GfCZlNLaiDgDOJnSENKTgW8AHwEqfY1SlXJqXKY8tmMpDYFl9uzZdHd3V38zTdDT09PsENQAvb19AGzs7d3yObTuO5P13pms985kvddXb29vdttXyGO6gYEBANavX1/I+JSvdvi+55o4RsRUSknjj1JKPwdIKT1Z9vj3gcuyf5cDs8sWnwU8npXPqlBevszyiJgC7AysyspfP2yZ64fHl1I6EzgTYO7cuWnmzJk1vMv8FTUu1U9X1xMATO/q2qq+rfvOZL13Juu9M1nv9dPV9RSwbVtaFJMnTwZgxoztCxmf8tfq9Z7nrKoBnAU8kFI6rax8z7KnvRO4L7t/KXBkNlPq3sC+wG0ppRVAT0QcmK3zQ8AlZcsMzZj6HuDa7DrIK4FDI2KXbCjsoVmZVEitMrxGkqSiS9sOMpNUB3n2OB4EfBC4NyLuzsq+AHwgIvanNHR0GfAxgJTSooi4ELif0oysn8hmVAX4OHA2MJ3SbKpXZOVnAedGxBJKPY1HZutaFREnA7dnzzsppbQqp/cpSZKkJvPaQSlfuSWOKaUbqXyt4eVVljkFOKVC+R3AfhXKe4H3jrCuecC8scYrNZONnSRJE+PoHSlfDZlVVZIkSWqEqNhvURyeLFarMnGUJEmSGsSeUbUqE0epAGxEJEmSVGQmjpIkSWobzqoq5cPEUSoAr3eQJElSkZk4SpIkqW04OY6UDxNHqQC8xlGSJElFZuIoFUh4GlKSpLbmyWK1KhNHqUCSrYkkSZIKyMRRKgA7GiVJklRkJo6SJElSg3iyWK3KxFEqAEeoSpIkqchMHKUCcXIcSZLamyeL1apMHKUCcXIcSZIkFZGJo1QAdjRKkjQxnnqV8mXiKEmSJEmqysRRKgBHqEqSNDGtMnjHUUZqVSaOUoE4Oc749W4e4LrFK5sdhiRJytHNDz9D94ZNzQ6jo5k4SgXi5Djjd+L/LuKvz76d+x5b0+xQJElN1CotqE39+G3qH+QD37+FY+bd1uxQOpqJo1QAdjTWbunT6wFY27u5yZFIkorANrX9DGbZ9uInepocSWczcZQkSVLbsEdPyoeJo1QANnKSJE1Mq3Q02iOqVmXiKBWIk+NIkiSpiEwcpQJxchxJkmrTKi2oTb1alYmjVAB2NEqSVB+2qVI+TBwlSZIkFZ6dtc1l4igVgMNWJEmqj6K3qfaIqlWZOEoF4uQ4kiTVxha0/VnHzWXiKBWIk+NIklQbW1ApXyaOUgHY0ShJUn0UvU31HHHt3HTNZeIoFYCNiCRJkorMxFEqEK9xlCRJqsyjpOYycZQkSVLbKPooHs8Rq1WZOEoF4uQ4kiRJlXmU1FwmjlIBePZRkqT6KHqb6jlitSoTR6kAbEQkSZKqK/g5gbZn4igViJPjSJIkqYhMHCVJkqQG8Rxx7Ryg1VwmjlKBODmOJEnS1jw8KgYTR6kAPPsoSZJUnYdLzWXiKBWAZ9IkSeoMtvlqVSaOUoE4OY4kSVJl5tzNZeIoSZKkltcqPXmeIx6/ZMpYCCaOUoE4OY4kSRNjXta+rNvmMnGUCsCzj5Ik1YenYKV8mDhKBWBHoyRJE9MqJ2Ft82vnpmsuE0epQJwcR5IkaWsm28Vg4igViNc4SpJUG5vQ9ufp9eYycZQKwI5GSZLqwya1fXluoLlMHKUC8CypJEmSiiy3xDEiZkfEdRHxQEQsioh/yMp3jYgFEfFQdrtL2TLHR8SSiHgwIg4rK391RNybPXZ6ZBeCRcS0iLggK781IuaULXNM9hoPRcQxeb1PqZ68xlGSpInxXGz7sU6LIc8ex37gsymlFwMHAp+IiJcAxwHXpJT2Ba7J/id77EjgpcDhwHcjYnK2rjOAY4F9s7/Ds/KPAqtTSvsA3wS+mq1rV+AE4M+AA4ATyhNUSZIktZdWOffqj9nXrkWquG3lljimlFaklO7M7vcADwB7AUcA87OnzQfekd0/AvhJSqkvpbQUWAIcEBF7AjullG5OpZlDzhm2zNC6LgIOyXojDwMWpJRWpZRWAwt4NtmUCsvJcSRJqo1NaPuziptrSiNeJBtC+krgVlhnphwAACAASURBVOC5KaUVUEouI+I52dP2Am4pW2x5VrY5uz+8fGiZR7N19UfEGmC38vIKy5THdSylnkxmz55Nd3d3ze8xLz09Pc0OQQ3Q19cHwMbe3i2fQ+t+bPr7+wFYv24d3d0N2aXlynrvTNZ7Z7Le66u3dyMAfWVtaZEMDAwAsGH9hkLGV2Tr+0ptPSm17LZrh+977kdZEbED8DPgMymltVWu4ar0QKpSXusyzxakdCZwJsDcuXPTzJkzR4qtqYoal+pn2rQnAJje1bVVfVv3o5sypbQb236HHdpme7XL+9D4WO+dyXqvn66up7LbrkJu18mTS1dgzdh+RiHjK7LJvZtLdyJaetu1cuyQ86yqETGVUtL4o5TSz7PiJ7Php2S3K7Py5cDsssVnAY9n5bMqlG+1TERMAXYGVlVZl1RoTo4jSZJUmUdJzZXnrKoBnAU8kFI6reyhS4GhWU6PAS4pKz8ymyl1b0qT4NyWDWvtiYgDs3V+aNgyQ+t6D3Btdh3klcChEbFLNinOoVmZJEmS2ljhr4MrfIDF5aZrrjyHqh4EfBC4NyLuzsq+AJwKXBgRHwUeAd4LkFJaFBEXAvdTmpH1EymlgWy5jwNnA9OBK7I/KCWm50bEEko9jUdm61oVEScDt2fPOymltCqvNyrVi5PjSJIkqYhySxxTSjcyco/yISMscwpwSoXyO4D9KpT3kiWeFR6bB8wba7xSMzlCtXbm2pKkcjap7cemvhhyvcZR0tiY/EiSJFXnSYHmMnGUCsTJccbPTSZJUmfwPHtzmThKkiRJkqoycZQKxMlxxs9NJklqJTZb42dbXwwmjlIBONxSkiSpOg+XmsvEUSoAz6TVzqRbUru4fdkqfrZwebPDkArLw6XmyvN3HCWNk5PjSFLneu//3AzAu189q8mRSNK2xtTjGBHTI+JP8g5GksbL3lpJEkCyP6p9WbWFMGriGBFvA+4GfpX9v39EXJp3YFIncnIcSZImyNE7Ui7G0uP4FeAAoBsgpXQ3MCe/kKTOYxtXO7edJGkrBT8JW+zopJGNJXHsTymtyT0SqYMVvI2TJKnwwjk3pVyNZXKc+yLiKGByROwLfBr4bb5hSZ3JyXHGz6RbkqT25vWrxTCWHsdPAS8F+oAfA2uAz+QZlCRJkjQeJhdSvkbtcUwpbQC+mP1JypGT44yfnbSSpK3YMEi5GMusqgsiYmbZ/7tExJX5hiVJkiS1H08Sq1WNZajq7iml7qF/UkqrgefkF5LUubzGcfxsfyVJW7FhaDtWaTGMJXEcjIgXDP0TES/EmYQlSZJUIM6qKuVrLInjF4EbI+LciDgXuAE4Pt+wJGls7KSVpM6y6PE1zDnulyx7ev1W5UWaHOfiu5bzR1+4nL7+gWaHItXNqIljSulXwKuAC4ALgVenlLzGUcqB1z1IklTdzxY+BsDVDzxZ+QkFOKN46hWLGRhMrF6/udmhSHUzYuIYES/Kbl8FvAB4HHgMeEFWJklNZ64tSSqqIvWCtjK3YjFU+zmOfwKOBb5R4bEEvDGXiKQO5uQ4kiS1N5MgtaoRE8eU0rERMQn415TSTQ2MSZLGzFxbkrQVh6JIuah6jWNKaRD4eoNikSRJklqeM7yqHY1lVtWrIuLd4Rg6KXdOjjN+bjJJ0lYKcMjqtY315fFRMVS7xnHIPwHbA/0R0QsEkFJKO+UamSRJkiSpEEZNHFNKOzYiEElOjlMLN5kkdZZW2O9XG6pq55laVbWf49g3Ii6JiPsi4scRsVcjA5MkSZKGGynxMiGT8lXtGsd5wGXAu4G7gP9qSESSNA4eKEiS1N5s6ouh2lDVHVNK38/ufy0i7mxEQFInq+Xi757ezWzcPMBzduzKISJJkoplpKGqrTCEVWpl1RLHroh4JWwZpD29/P+UkomkVABv+Pr1PL1uE8tOfUuzQ2kKDxQkSZLyVy1xXAGcVvb/E2X/J+CNeQUldapaJsd5et2mHCKRJEkT5eUUaicjJo4ppTc0MhBJqoWNsiQJbA/amXVbDNUmx5EkSZJailcwSPkwcZQKpJbJcTqd1zhKksoVoSW1bVI7qpo4RsnsRgUjSZIk1cJkrX2lQpwOUNXEMZW6P37RoFikjlfL5Didzk5aSZKk/I1lqOotEfGnuUciSZIkjdHwE4dFPJFYKaQiximNRbWf4xjyBuBjEfEHYD2la45TSunluUYmSWNgJ60kqZzNgpSPsSSOb849CkmSJEmqxF7aQhg1cUwp/QEgIp4DdOUekSSNg0N+JKmzjNajaLMg5WPUaxwj4u0R8RCwFPg1sAy4Iue4JEmSpDHz0gUpX2OZHOdk4EDg9ymlvYFDgJtyjUqSxsgDBUkSFGsESqWmqUjxSbUYS+K4OaX0DDApIiallK4D9s85LkmSJGncinA+sVqO6G8Sjp9brBjGMjlOd0TsANwA/CgiVgL9+YYlSWPjGVxJUitwhIxa3Vh6HI8ANgD/CPwKeBh4W55BSZIkSa3KHFHtaMTEMSL2iYiDUkrrU0qDKaX+lNJ84G5gZuNClKSReQZXklTOgShSPqr1OH4L6KlQviF7TJIkSZJy5WUpxVAtcZyTUvrd8MKU0h3AnNwikqRxsDGRpM400iQzRRqIkio0UrZbalXVEseuKo9Nr3cgkiRJkqRiqpY43h4Rfzu8MCI+CiwcbcURMS8iVkbEfWVlX4mIxyLi7uzvL8seOz4ilkTEgxFxWFn5qyPi3uyx0yNKVzRFxLSIuCArvzUi5pQtc0xEPJT9HTNarJJal9c4SlJnikL1LUrtr9rPcXwGuDgi/opnE8W5wHbAO8ew7rOB7wDnDCv/Zkrp6+UFEfES4EjgpcDzgasj4o9TSgPAGcCxwC3A5cDhwBXAR4HVKaV9IuJI4KvA+yNiV+CELNYELIyIS1NKq8cQsyRJklrA8KGqRRoBGp7VrCt/+7IYRuxxTCk9mVJ6LXAisCz7OzGl9JqU0hOjrTildAOwaoxxHAH8JKXUl1JaCiwBDoiIPYGdUko3p9Ig8XOAd5QtMz+7fxFwSNYbeRiwIKW0KksWF1BKNiW1Ia8VkSRJyl+1HkcAUkrXAdfV8TU/GREfAu4APpsld3tR6lEcsjwr25zdH15OdvtoFmN/RKwBdisvr7CMpDblkCVJ6mxFbwU80alWN2riWGdnACdTGk1wMvAN4CNU/q6nKuXUuMxWIuJYSsNgmT17Nt3d3dVib4qenkq/iKJ209vXB8DGjRu3fA7HW/dF/Pw2Qn9/PwDr1vXQ3T25ydFMnN/5zmS9d6aR6r1T9+djtWnTUJvZu9W26u3t3XLb7G04ODgIwNq1a+mOTQAMDA4AsH7DhqbH12rWri3VOSm17LZrh/18QxPHlNKTQ/cj4vvAZdm/y4HZZU+dBTyelc+qUF6+zPKImALsTGlo7HLg9cOWuX6EeM4EzgSYO3dumjlzZg3vKn9FjUv10zWtNPp7+vTpW9X3eOq+Uz8nU6eWdmPb77BD22yDdnkfGh/rvTNVqnc/C9VNmzYNgOnTu7baVl1dK7PbrqZvw0mTSleD7bTTTsycOQOAKZNLJzdnzJjR9PhazQY2lu5EtPS2a+XYofqsqnWXXbM45J3A0IyrlwJHZjOl7g3sC9yWUloB9ETEgdn1ix8CLilbZmjG1PcA12bXQV4JHBoRu0TELsChWZmkNuTQH0kSFGtynCG2UWonufU4RsT5lHr+do+I5ZRmOn19ROxP6bu9DPgYQEppUURcCNwP9AOfyGZUBfg4pRlap1OaTfWKrPws4NyIWEKpp/HIbF2rIuJk4PbseSellMY6SY+kFuU1jpIk8GeapLzkljimlD5QofisKs8/BTilQvkdwH4VynuB946wrnnAvDEHK6nlOVW3JAmK28tX1LiksWroUFVJqjfPLEuSoPizqm5hAjlubrJiMHGU1NI8gytJKqryNsoTnWp1Jo6S2oLXOEpSZ/M8opQvE0dJbcFrHCWps4w04qRIPXu2TWonJo6SWlqRDhAkSVL9Ja9LKQQTR0ktzbakuDYPDLJm4+ZmhyGpTY104rCo7UJR45LGysRRUlvwGsfi+fh5C3nFiVc1OwxJbWp4IlbEVqBSsujwVbUqE0dJbcGGuHiufmBls0OQ1EGK3gp4aYVanYmjpJZmQyypqK5d/CTv+5+bGRwsekrTXurdLvQPDPKu797Ebx56atzLWvP14TDfYjBxlNTSbEwkFdUnfnQXty1bRW//QLNDaSvR4DOGT6/bxJ2PdPO5n97T0NeVisbEUVJb8BpHSUXjEPr2MJF6LJ8N1BOdanUmjpLaggdokiTIL0EbzwnKap2iJpAT4LZrKhNHSS3NaxwlFZUjIdrDRBK98kVtr9TqTBwltTTP3EqSyuWVoJn4FYB10FQmjpLagmf2JUl5mMj5SU9uqp2YOEpqC17jKEnK00RPT5pE1oHbsKlMHCW1NIcOSZKAgmZmRYyp9RSyajuQiaOklmZjIqmoHAmRr1bduq0adyF4sripTBwltQWvcZSkDpfTEJQ0gTOU5Ys6QkatzsRRUlvwzL4kdZZG52ExjszPJDEnNvVNZeIoqaXZOEtSZ2pUDlGv33FU7Tw5XAwmjpJamtc4SpKAwjcIBQ+vNXiyuKlMHCW1Ba9xlFRUJgyNVaT2oFLd+3lQqzJxlNQWHMYiSZ1htLSw3u3BUKI30UsjvLSiDmzqm8rEUVJLsyGWJAGFbBA8qVkf9tIWg4mjpJZmYyKp6NxNdZ4iDZdtK27WpjJxlNQWbKQlqcPlfCZxPB2alXoaPdGpVmfiKKktOBxIUlFN5AfkNX71PpE4kfbFqq8zt2dTmThKBeB+sHYFvKRFktSGxpOQVn+urf54ucWKwcRRbet/73mcf7vs/maHUQiPrtrAUd+/hZ7ezc0Ope5GO5t7+jUP8aNb/9CYYAruqZ4+jv7Braxav6mhr9vqvS0X37Wc/7jigWaHMaKBwcTf/2ghdz2yutmhtIVf3PVY3eu7tb8BrSevWVUnuqwnOuvAbdhUJo5qW586/y5+cOPSZocxJnnvB09b8Ht++/AzLLj/yZxfqXlGOrt72oLf88WL72twNMU076al3Ljkac6/7ZFmh9JS/vGCe/jer/+v2WGM6Mm1vVx+7xP8/Y/ubHYobeEzF9xd6PpWFTlnZiZ+BeBZmKYycZQ6SIt3/FTlNY7F1c6fO2ks/A7kY5vtWsANXd42FTA8aVxMHCW1NM8AS5LKOct2+2n1Sx7ahYmjVADuDmtnW1J8VlG+3L7F5f4pX406cTiRaqz0GfBzMQGeE2gqE0dJkqQ8mSjkotEJ2ERzFkfI1IHfpaYycZQKwLakDmxMCsshRpJamfswqcTEUVJL8wxundwxD278VuXHrvwiPPC/jY1HagOv4kG+MfW7pDTY7FA0AUNpY4yjwbFtqq8Jpe73XADXnlKvUDqaiaNKNq2H60+Fgfb7nT+1t5Y5Efzgr+D/fp3f+lOCm06HtStqW/6yf4SrT6j82M3fgQuOrj20mpdU3fRvguv+AzZvbHYkxTQ4CDd8DTbW97cwz5r877x78o2weUNtK9i4Gn79tVJ8elYTkrK/nnwFzxtcOe7lytuoQrdXQ21IzxPNjqS6Wur+4mPhhv+seyidyMRRJb/+T7j+P+Cu85odSUcqcluiOjn//XDO2/Nb/zNLYMGX4MIP5vcaal23/wB+fWrpwFDbevgauPbf4PLP57L6mhOGy/8Zrvs3WHJ1XePR+Exev5ITpp7Lf2w8sdmh5OepxaU25KcfbnYk1XnA1FQmjioZOhs6sKm5cShX7Tx0puPbkqHRAn09zY2jgkKfZe8U/Ru3vtXW+vtKt5vWNzeO4TatK90OOhqomdLgAAA7MP6e40q/MVzIXeJgf+m2gG2IisPEsR0N9Dc7gtYwOFiY4T9tnM/lrlDJcEqQHWCoPfQPjLKPKFCdB5RiMVNXhxielOX1yY/Un61/Yg1OodqrFuNurRhMHNvN6mVw8m5wz0+aHUnxnfJc+O8Dmh2FJqhQjclPjoKTdm12FIVT6Yx7K3h01Qb2+eIV1Z904QcLU+fbpU2lWK7bdhKIx7pLPY3LV9d4rV2be2pdabTNijX17ZGN7LPfmt8AAdC3jjnnHgjUVo+FaqPagcl3U5k4tpuVD5RuF13c3DhawcAmeOahZkehdvLg5c2OQHX00MoxDNkq0GyzM8iSnjvmbfPYI6tKCePy1Q5VrWQooX5ibV+TI9F4xLAsIpecYuOquq3KJLIO3IZNZeIoFYD7wYnr+Aa5wGOgOr5uCsSqaA5/BzAfjR7NUMtQVWte7cTEUVJLK3C+JEkl7qg6TrUaN5mshVutCEwcpQLwkEKS2peHvI2Rz3aOEe6PTXlvs+cP6sBt2FQmju3G4TDqMH7kJRWeO6oxW9/Xz8q1vRNahwlaG/Or1FQmjpLaQqvO3NkJPGaWNFZv/a8bOeDfr5nQOtznSPkwcWw3nmZrSbZxUvG05cFnO76nuspnAzk5ztgtfXr9qM8ZPpvqs+X5mujkOH4Maue2KwYTR2m4Zx6GE3eBp1vspzrOOgyu/bdmR9FwnisZOzfVKL77Grjp9Ia+5Bcvvpe/mX/HhNZhUlJcMVIi+sBlcOoLoH9iQzLbzg//kmVdR/GL7f616tNGGmHy4pW/5PZpH4c0WL+YbGSKpUjVcckn4YIPNjuKhsotcYyIeRGxMiLuKyvbNSIWRMRD2e0uZY8dHxFLIuLBiDisrPzVEXFv9tjpEaVvcERMi4gLsvJbI2JO2TLHZK/xUEQck9d7VJu672elRud3FzTsJeuyH3z0Frjha/VYU0vxmLlkYLC0Ifr663jAVCctM4x45f2w4EsNfckf3foIVz/wZENfU9XkdVQ67Duw4EvQu4ZJPStyer0W9YebANh/0v/VtPhf/N+p7BFrmDK4qX4xTbCRqbS47dYEFGnb3XUuPHBps6NoqDx7HM8GDh9WdhxwTUppX+Ca7H8i4iXAkcBLs2W+GxGTs2XOAI4F9s3+htb5UWB1Smkf4JvAV7N17QqcAPwZcABwQnmC2vbcG0kd6dHsB8xXrPEH3iW1t5GGqm55vEi9UmWKGpc0VrkljimlG4BVw4qPAOZn9+cD7ygr/0lKqS+ltBRYAhwQEXsCO6WUbk6lsTjnDFtmaF0XAYdkvZGHAQtSSqtSSquBBWybwConvZsHuGnJ080Oo+WY7k+c50yKq1Xrpi0P8trxPbWCwdq+BIOt+uVpujputwnvCKzDenArFsOUBr/ec1NKKwBSSisi4jlZ+V7ALWXPW56Vbc7uDy8fWubRbF39EbEG2K28vMIyW4mIYyn1ZjJ79my6u7trf2c56enpGdfzp6xfzw7A5s2bWT+O9zO9r49pwIaNG9g0ge3w5V8+xKX3ruTiv30le+82o+b11NNI9TpzhMen9W5kOtDb20tvgz4Tfb2l61x6e3u3xDPeuh9S6f1u2lQatrNhw4ZCfs4nYmCgH4B169fR3T11xOc14n2P9Jka7bFytdb7unXPTigx0mv09vUBsHHjxm2eU4/YR7JmzRo2bzd59Cc22fD3Wb5Ny8urLVOrWusdYG320wWD2XVdgymxdlg8/ZtL35OB/oG22wfUw9A+OA0+O9S7HtupK7td27OWqVOf3T/tODjIZGD9+vUMVnidodde8cRaXgzc+8hTzH5e+9fbzLL71bZ/X1/WZm7s3ep5M7IzI70V9nG1ip617JzdT8SY1zuQfZZ6etbR3V3qp+kfGABg48bitcWTenrYCRgYGKCnYLEBrF1bGlWTSOPedvXaT090vRPZzxdFoxPHkVQ6nZOqlNe6zNaFKZ0JnAkwd+7cNHPmzEpPa7pxxbX99gBMnTp1fMtNmwbAjOkzmDGB7fCH7lKCkqZOH9/r52i0OLZ5fFqpqe/q6qKrQe9hWtezr1keTy3bsNIy2223HQAzZswoTL3Uy+TJpd3YDtvvUPW9NfJ9TzSOWmLt3v7ZEzUjLd817QkApk8f+fuZxzbcaeed2WFaUZqb0Q29zx126KtYXm2ZWu244441r2NdKh1QTYpJ2W1ss64pU0vbf/KUyW23D6iHoX1wTHp2IFY9ttPQ1Dfb1G/2Ottvvz07VXidnXfemYhgaX/p8GUzUzqu3qq932lZOz1tWJu5Obsd3pZOSKwbc1zlJmd1vMMOz7ZNkyeVTqBNn17Atrh3RwAmTy7mPmKnvtK2C7bdv41VXu9rPOst4rYdj0bPqvpkNvyU7HZlVr4cmF32vFnA41n5rArlWy0TEVOAnSkNjR1pXZ2hLcdWtT9rTZI6iENQc7Hl5zJy2r5pHMdYUeW5LTNhmDRMoxPHS4GhWU6PAS4pKz8ymyl1b0qT4NyWDWvtiYgDs+sXPzRsmaF1vQe4NrsO8krg0IjYJZsU59CsrDMUpDFq6UTI5LslFeOTr0r8uQh1vAl+B2yVKtu2uY4Ryif0KiPcH5vymvfwonY2I8WQ589xnA/cDPxJRCyPiI8CpwJvioiHgDdl/5NSWgRcCNwP/Ar4REppIFvVx4EfUJow52Hgiqz8LGC3iFgC/BPZDK0ppVXAycDt2d9JWZmKbvkdHfk7hDCBpGdgM/8+5fs8j2fqGU5LsSFWXiZ0oLJhFfziE7Bp9B8zr7tqgXvwNYqceqqG18koOy4PksdmpO004u9n9m+CSz8FPU/U9no1LdUAK+6BBV/2g6Pc5XbRSUrpAyM8dMgIzz8FOKVC+R3AfhXKe4H3jrCuecC8MQcr+gcGmUJpUoVGd0Nv8YPso/HG6j/8qzIPX8tRU67jebG62ZE0je1kyXg2Q6N7ADuyim74Otx9HjznxfDaT474tFfF75kRfcBBjYtNzTfO72BHfodqMOpQ0gd/CXeeA71r4H3nNCamVPl+XZ11GPRvhD8/Drab4MSENqqqomk5gorl3uVrALj7kfrMONXSux13mpImLNuPjHIg+/NpX+G87f6jvi9tN3zhJAYrP2Bd5aTT2vGx7W+kiTJxFACbsimiNw2M0LgpV43a1bdzTtzp19EV+XChw6smd2nLMWORPwWdadQaGeHLUW36eI0u6rnTKftepVqucXQHWBdOKFQMJo5tq7nNjY2dGsVjZWmIB1YTl88OJQ3WVjfWaK2av+WqfpKaH55UExNHqQBsQ2rnydwWYB1JlXnmqyYjbbahHsERJ8eZ+CuPewlnVVU7MXGUpDZgbiaPSYtrxERmpKGqWbl1WtnIJwyzxLGeZxQ9OyltYeLYZvoHS9co9m7uH+eS9W2e3M22v7sf7eaqRbVNaV43N3+Xl/XdCbTWZ27JXTdwyw/+aauypYtu5ebvfYo02H7XGXfUtSkbVsGln4bNGxv2kh7XFt+WKnrqQbjKmcPzl9PPqtR0jWPl+4VV0G7Rlth2HSC3n+NQcyx6fC2vABY/0cP+zQ5GY1bzbrqJe9J3/PdNACw79S1Ni4Erj+dfgR/w4+bFUIO9f/F29okEg1+HSaXzdzN/+m72podH13yaXXbdddzrLGZT34GuOwXunA+Tp5X+b+B3NL/heZqwoc/Bee+GNY/C5O1K/xf0IL3oRt9so3wXxvW9bIHvlVmVGsQexzZTlH2HTaE0ssGhXe9A35ayyUPT9U+qbbdckK9+RUXZLzXEljfbuDe9TY9uR23w1rBlZs2BzaXbqP49twZrM/Q7jkU6idLQERd+95UzE0flYkK7rsLs+BqX/tb6jpc+s76ucagx+phautPf29DXtXOjgZ79jYyGveRYDpb9DDSbP7RRCOP5IuRwTFL3NW55P0U5flK7MnFsWzZKnWBt7+Zmh1AcLdRebsquEkibG5s4NksLVc3EDT8gbcCJsPG8RGHOyxVWfTfQUDL/bEe0P9Ser6HJcUa5VrxJXwSrvXbuu4rBxFHF495BbW5T1uOYR+JYy+QNjXDIN67nrf/1m2aH0Zbsw2oFw4cwV68tm8HaPLt167kBn11XqiXza+TkOBN4gZ6+0qSKqzdsqlc0akMmjiqgorSYjZzUQu3mNw89xVd/tbjiY30pSxz7+yo+Xotmfoaue3Al37jqwREfTynx8FPrue+xtQ2Mqtma0bNkb1bhbelxrP/hV/eGTfz9jxayZoMjUYp0jWNj1f6+V64ttUer15s4buWyf4SrvtTsKArDxFG58LClQNY/zbue+BZTGe9PtGgiPnjWbZxx/cMVH8ujxzG3w6QxnMH+6x/ezn9duySvCFpMM4aqbv0aA4OdetCcn/Nve4TrHlxZ+wqG6mhoCGUOyf1ZNy7l8nufYP7Ny+q+7tZR2q51/dpNcGV+G1vcHfPgt6c3O4rCMHFsV/Vuk5YvhEduGfPTW25ynO5H4P5LhhUWf3KcMbnyC7xu9cUcO/ky9nzi2jxfqala6bcCh65xZMBrHFvaXedtW/bIzaXbJuzH1mRDzNb2jnySaNa63zUqnLZy/M/v5a9/eHsd1lSAb8P6Z+CenzQ7ilyN2uNYc+I+juWqPHXE3cNdPyr9Fmyt2niMcyu18e3MxLFd1fv79YM3wrzD6rzSkTRh53Dm6+HCDzX+dSdozfoxDEkaLB1Efn7qhbz29k/lHJHGYjOTAUj9LTCkrI0PRCbk6Yfgkk9sW/7kfQ0P5dmr50avq9k9d+cbjCra0iuchibLqT55y/C6rOtpzAs/BBd/rHTCtO2McXbRGn/HsZZryMtfqmq+unIxXPL3pbqZyItIOTJxVNsbPoyrog3P5B9IDs6+eVmzQ1BNhoZTVTh4LNwBwMTjKdxbqocG/5TKWHTudV31lPdIk1Id9W0eAGDVWE7+Mbbv0Ji/Zz2Pl24HWuDE1Tht+R3H0WZVrXX9E12+2gr6N5Zu1z05kVeYwLKda11fP73Zd1LVmTgqU6CdTZ2PMse1usHm7DhqPVQp6gyaaiNtmfU1QuO227ZVZJ0VzlAis6XjsXRn41gPVvPY1bfxs+54cgAAIABJREFUd3vUzdXA33Ec9xDLWl5v6P3UoU478ahivxOu5JBv/LrZYbQEE8d21dLf/Ao7vq/sDNeclP9Ln7Qrm2//IQBPrG1cj0L7Nt+Nk8sx0O1nlT57m9bnE0yl5zVgRsx6b6t/m3IWS6YdvU35cVN+zLKuo5p+bcrgYGLOcb/khzctrd9KR9uITTgob+ndftsa9jMcY+wJq+Xj89z1D7Cs6yj2WFd5Nud2MtpnfdTe9xqHqhb2W9bGJwGG5P0WH+vemO8LtAkTx3bVjvuQ33yjpsXGuymmrl8BwNKnx5ksqP3c9K3S7fqnxrfcGFu4isOoC3cAMHo8R0+5himx7QHx3025LI+Axq0/m2X0lF8+0ORI8jJ6HRX0cLeA8vn+Pfu1Hv57jvWz76rrAZiz6saxLdDCP9sy2tZr9smqcuPepU+oXorzvtWeTBzbThvsNAp34Fxcr5t07zZlq9Zv4psLfs9gkabkv+cn8Psrc1v9Hqxmn7v+I7+hxjl9Jut5jeNYDjWmDqznhCnzmTKe2Vzr8d4TvHXSzRw26baJr2sCaj0ee+2k+/jA5GvGuVTjflNxy08DtsP+vwBeM2nRNvX93snX87pJtcxIO2yylqyyJo2QQL5m0iKOGvdnbatVd7Shyzeinhsjhw1b98S2jkNVpWqmNDsA5aR1TyRua6LXF6RE0TdIrdF9ZMqvtin74sX3csV9TzB3zi68bt89JhZYvQzNEveVNbms/j+nnskLf38PLD0C/ugNdVxz7Vef1vqsWg8oxrLUAY+dy2unXMlNj+8LvKym16nVd7b7r+zeiQ193Xr48Xb/nt07bfwLF+RAzuuhx+787U7J7j1b31+bemZ27/hxrm14/Vf/f+i1e/naOF8Hxr2/Kshnsxajv9OR3tvEvgc1zapa/up+DdXi7HFsO+2wVxq2w5/g7Git2zTWZsOmUq9bf5F6HHM2haGexrzec06TG1R4Xp7HcpMofZcmpfH0zLb+52hMyfi4N/zw/VTzttPQK7fD3r9dDf85jlx6h/0AMLQRJo24fRv3Pd1j8Blmx9YzpDZsN/HILTCYz8yy6mwmju2qlY/1hu9ZmzTT6USklFi9ftPYnw/sxLr8AlJtaj49PNYvYIXEsdYv7/DvTe+abQ4caup1qsORTrN3Ry3csaJm6h/7Pnzsth6i+v/Y++44O8p6/WfmnLO76QFBQECDgr2gIop61Xu9tuu1/dR7EVHuxYYioteGIkgLHSlCQCTUUEJvgYQkECAhCaSQ3rPpye5ms3331Hl/f0x7Z+atc+aUXc7z+UDOzrxt3v7tyapUaqZPWvxVGLL/qyE8VdWK7TrqfTZ94HS83PwbtZBgqigMAQWGqQFdx6bZdtztV/+RXL11grEY9BigDdQGDcKxgfqHlnSEkb0Gl8a7F27Hhy+eja0dasTg27tewcqWn+KorvJtwOr6jrx7aa1bEA8Vs3GsUF3ZHuDytwJz/sqrufw6RhrK7fdaShw9G8cGEsXUf0+sKF/iGLz08mi3eNPJlWYqNypOJXxceiRw2VHJlikBjygzk1wM1V7bovomHw5c/U5x3u7t9u/9G5NtV61hFbG65ce4MHVbrVvyhkaDcBxxGAk3iKRVVat/oZu3oR2AumfWI/tWAAAO6406u4mLupwCbWtq3QJNxOxFVa+qrLkZ2zmOny/b3wUAGFj2UCSV/39VJCBxrDGd6hNWlVwVtSfGjYbSarLYuyL5Mp3zbETORFICrGKtarfhdWyFmH1xlDZYz8ppXo7hK8DjQNR+H6oUDGdufcN4ucYteWOjQTg2UH8YAaqqKYfdqWpmmCxztH4PjsFCZVRMKqeWFLdcxXxJ2qFSRR3ozwEA+rIFdhKNOcL0/DrMoMI80v3OfFFtXxrIV/4iHf2++t0D3rCI2Dja843U0hZ9BHhqMULf4G1t3D0uzjcnN0ZKXR5nXBLxfl3f+0adN+8Ngwbh2ED9w1NVjXfI1WKzMZ2Nv1TDS0H4QK0HrN7dV+Ea6u+bWfBsDasex7HS9j9s1DqmmkqX6jJcdnYNKlWyandlPAmz0AjHkQQqvYeoqZPWes3UO5pKA/hL+h6Y4dBChkyrovr9alC2shW/jzSoqwYqjEY4jgbqEGGJoyMJMIYPn8OVOKoTjm+QzX540HUUqq+qGl9izMgXZh7EYCYQa/gHchjpq6txVxwOCEocI88TRM1sHKuIk3bdgY+nn8VLe98H4P3ec985ToKaEoF+0t8NUzpxc+MiAVXVNwLjiRBSl0z14YThcxNvoPrI9QPt66pfb/gwI8OPcDQ9VdWRvxHroPIbdr30N8F4DODtxh5xKlY4jtg12jkPszpgDHRwUsW5XIwEG0e57bcuwR61l6yHuVcPbWiAhrfifD1x53nlwnK8EWaB6YRgMkkRG9v6MJALqYQnqqpaJqxKeOcNIZFNduTPnFqfRSMBw+cm3oAS3MtPImvj/pOBKZ/QWmkV2ZJdVdWYhGMtVVVVCcfhL9NRg1kh4r9i/VcGoft403l4vvl3klQsiWNcTrld1mgM4YiHvspJoe8cp55tZlWh8gXD+TtD5nNMvBGkCfWNYP/z4ww6qSOCyQqM3zCWvHimjJaFL177En52j+uxuxJOYgj1S7/PTKsgT5QUCBmx1BFJQIo8MnumumgQjiMU+lsbI8c2x3OVjiMN7XoVSrHKJBxrsFWknO5UVVUdvse3Hip9T+kPc52Tgu5BTAjebu6LV2zMQ5+VjUssVPliUevDWu1z9cc4RiUVAamg9KqBpMAbmxqO2bAmMIJ24ou2drLe1gUMXYljrcYlScFDJZBAvzS0wMpHg3AcYYivCihaTLXWMyuPcKwFTE2vqpVF/RyhhlmZMXQvzKrhT3RKjgdFSXOCqqpqgcT1ufGJeFWt9WGt4hyn8q2oOEbCN4xY8NZAgmtDnXFQP2dCXBDnPmCghG0tp+B0c0Y4RYKVlSdxNEpVlDiCDGtJsghJMMZqfRSNBAyfm3gDtUOtg9+WaeNYU1VVRcqxEk30j4762SmNihP/lTowK9SHjMmpRgAyilJJ43kc1KgjgQVU65AeSuE4yuby1FDi6Jlw1s9abyAI3ZGp7EgO/3niEnCmE9vv96n7nec24u6jvNrKgbaqahzCzwhKYONh+M8LGRreistHg3AcYaiMnU61FxpHVTUfL5RDLbaJtOtVtUpU63CxkVQ9D3dtXo2Fd5+nXO6nU2titkiCuIcxzaEW5E3CZsOvR6esKquf15jNq+AbZ2SgcSdKAJXpRB4hozwnFTbP4XIOJAHfXpswn9fVYqiGxDGBPdadYvU6i5KIeVrVo2jJHcCuJVWssDpoEI4NyFFz5zj+hbh3/95K1JA4TO1wHOVBJGmoZSzJMFRVqdPTvoGTtt6Ans62CrdIFfHlBcLlw3iXLJ/cEP5dLdTa8YySNJZBxG/p6NeopB7WWT20oQE91GLM6pU00ACXqSfTqojR33QdMaSBZjW8qnqI7xwnCcKsshhmhOPTvwZu+3wVK6wOGoTjCENlwh3UWDrhShwBkKJ+PKRaXFodulHZELuSKmbr9sWT1FYCqqqqozAUt4aY+SQoQ+IoSRh9EvPwZs1z3rzS8qqaxGWi1oSjQv2sJJc+s56fPtK3dXDpqgvitQEWdFXk4pxbbyRVZY8J5vhAiHx5jdXjAcByiViGqmriSzURVdV6R8M5Tj2gQTiOMCQajsMvNMnSYtRPHQCFGIRj7Iqp67VlAXl1xyspAKORVZb2NVk2oWSS5L2C5gql4IPweOY0pCplIsDXsEpAftAe08ED7PSahGDFLk5lXEJYLXIvPfTlcBSyzrtKXnhG6OWiVAQKfGaD0tcy+iSNIg5Cb/x2VQmi4YzEt4uDXH99zRnJeDNRxX2OhnxPSq5f/ZJk+6ZGnXH6WhWFIbt8DoTEs+sch5TY71XiOJYKineKeM5xikjbNZZ8iWMs3n4xBxQlUkueVFTSxx5KRZgl/ftVVZGEvT3rYSGLNKg+yg/Yd74GmGgQjiMWSUpeaq2qSh0MMSSOZVTs/5zzV+DStygTjx878CTWtpyOMQM75Yk3zcFH9z0IADhx+61xGhqA1t66aQ5w2ZHA9lfKrlcFAYnjE2cClx4B3PNN4MpjOBmq0iwFlKOqGs3rxnDz3uxYjAw4FyBFqHQV8S4UOloESRygFSY67v9vYPLh/NrVKMfIk5XNP8HyljPU2lBPhJWDlzd14H1/nYWFWzrliXno2WXvEYtvSa5h5eL+k4XjHcGOxfY3bJylkLgym05V1QCT/IR7v6PX1zqYfDgw7Vv890L78OBHRnc2hf7+578Ckw+TJovLfPEIR0riqLRNhBNd8mbg7x9RrDWUd/LhwPRT5dnu+SYmPfVdxTqGL5jMiMmH4emmc+3f2R77rvfC5Oo2bBihQTiOWCR4SNXaqyqlqopSruziYmGF7bFNlXB8d/dLAIBxA9vkibe9FLNRYniaK+FbBN0hbt07F1ekDdE2UW1x+3THwki7jJhjlrwNn9uJmsQTUby+uJfJXa8y82pVqXQxjeE4IhGvqhXeQzbPEdfvxjkUTA9WE0cbuVAaOlHYKUftCEfiMSKCbXBj2y3dzpboK6Frm/3vuqfil5E0Ns/WS++ur9bK7LUiSPekShCU0iI19smtL5TTEjkEYyLeNhw1UM7erPSF+1appEJ/Np5zG6/5SajN9igwoXnY+Kw8jRu3OyE8vHQXJp0zA9mwxlNZcPbxMkrgLbd3m07/DnXZ/658sIxaRjYahOMIg+G52w+hmAemfQfYuyJGqdW+EIXqK1NVtRbwzQ1qL4WIqkrVrk2m4s3d+2XWichx7ZOJFuepqjK94yQ3Pkn0XiLNqfU6sAhuzNyAT4B/USw3ZEgtHUuohON4I3ncrCcYHKLeB/t5ZNdWmF4jbYRFzBgvtFB43cbQqpCCOrdirSPd/a8cXxWE1H6/BXDt7I0AgP39+sz+ikLWNZ5WVO37sF7RIBxHGLiESvsam0v75FlxClVPql+6QqH+wWAU1W0tDkIvzk49koiuur5nUr04jlVFDQ8VsfMmv10TDFuyW3ZTsz34v/SDSMVVA3Xb+9KVmhlpVdXQq4B0TPCB3TuA+dcpd4KOSumerkG8snm/Yuok5kuN4zhaBfxnahH+aV5eXjmCrqiHlZ5YG7YvBFY9nFRptcPifwAdG2vdCgB8oj4R5uKSO4DuHXUxB6sFPxxHif08Uec4ZfasYIw7+3O48flNCTKZR/AsSMCHh1wzJKaW0RsIDcJxhEKonhijNO0cZVXHlzgSS93W4PLMbfhN5hGkdsRVwfD7sGfIVlXpHFDknlVZ4jhspAm6uoLldt+cC/Cr9OP4qrmozII0EZCehj5i2repZO47I5r+vpNt21pVFSWFviLOlr+3Zwin3KaonqzDOKoDTjcLrjRQ1Lry215LVVV2E2J/0h1fBh75URktqgMQAjz7B9uObSQj12e7/b/ra/4z5eOgPterCyJk+rIJRN/bav18G4tYcZ/9/uGVuPq5jViyvSu5CiviXT8ekhyGcrVCAAXN8DeEd9ry0CAcRyx4k76yahb09Tc+RLcf9faPgkPkWeXr2LstUO8K51Cr0v5Nc7Mjh1SkEeKP2NeTTdguga6Z3yHsQ6HMzdvxBthkJO+tVhXiOI6MC4X7LNenUEAgpzwFT5W9zHK9lJyktY4P5ktjBfNPqRwfYTvceiWagUqFaUoAB1qBbIW81rrjka+NN9UweGuAJwHx1Y8lcM832jO18pZRv3PWhlxVNf43aNxryu0nQX7X8U6xlNBY1P2YxkfZ4wCVfboCqs4jDA3CsQEF1HoBUfVrEbFEN0viqGXdhseR1RNDfOKyuThj2tKKtElMODKIqIQIDkLiXpwrfOFmfV5c5zgaaXXClug0p9Y7BReO5EIomdfs96gNGjt/EpcdaVucOoZdHL8bjgfu+EqFCq+/vmDOEUkzpV9BMQX0x7/++oiGkOcGcTiOyq0FnTOhRkTIiCUeEyAcZQkaEkcpGoTjiIWelElYRgz1gGRVVWl7MfW2eIRjbG4781avVcKwu8g5mLeho+p1Wgy1JG1PlfUiWFG2S0wwldLa0O+gJNSDai2NU4lvq9LGWn/HiETb6sqUW2djRQjBNc9F7S2V55TO99SrhFkXwm/mqaqq5PXzq8CI6+bba4pm/sRMi0bIPPBQ/pq2ZH3bcI4jRYNwfKMhzoFSR85xyiKAy8ilb0NY3Q1bj0AVqP9ojPUl6amY0fQnjXolEkeWHYiuY6OkJ2DMCxhNbIlVVRnEsrKOmhy8IpKaL5GU3I+tMeHoqPOJ558ewn3I+/b4jCt1uDVXhFFVZwSYMurQucX0JQx7Za5+t/2PdPaUMz71NrYkvKZEiXnSPFcdX/ZtOqoUlVNV1cHubgXngHUyptJtb/b5wC2f1iozkS+TaDHlSva+kSuUad6y+tHy8tcx0rVuQAOVQlhqV9Pay8zt/y3lFlEo195y2DickSBycRX0oY5m6KnpufptEZTPUksVShyrcUDGVRsl9LVGgVA3gmyKeJUqJIlj45iAqmoSUsty4NZfrqoqEf1VQ1VVr65hXHryqI8LtA+rwtGF4uwhddZHYcJR4I2ZGGxVVcIlKOPDCNwm4mhtMM62yBWNesChul7a2IHv6dWslbqqWHC9fp4E9lLZHWd/Xw5HAhjMFdBcTkXzLisnd12jIXF8w6E6znHoLMSy8Mpdf8H+vTvi1Uf/rUHZ+PGzanfhqZZqm+gLdaQQ+mFH9CCSvrAuCcL+qwrhGM9JkKUqcaw6VLnxNHQkjprNqRJUXPNrN11LOlJZ1Gu/1xR10in0WjOYO3VyTBXts65O+igOPCk7Vw09yW+jzGViaBCI9tvkFRJIXakq19sUk4atqrcG1yEahOOIRRI2jknkBbaueRWfbP072u44pez6LS0bR/dHDTZRnToT3qjkxYkkjhUmHEXvWBLHWkuqYnrkLSmq2DK/z32mbRbDujqx56Gec5wE5kStbRxVnOMoME2Cn8HXjAg8reL+M1xtqisCzb2j0n3H9arK1VTVlxoO9/EP74di52jsOI7e2zogAPy1n0xblHYSQmqy3xJCMGPlXo/5XIltL4n5LXO459Yx3NdSJVETwtEwjG2GYawyDON1wzCWOM8ONgxjtmEYm5x/D6LS/8kwjM2GYWwwDONL1POPOuVsNgzjBsPxOW4YRrNhGNOd54sNw5hU7W+sW1TYxtHPQhF7JTsGYnNpUDU3v34d6adj0B7foyajzGG0l3jOwUQxPUP9WUvC0XsbaF/80mjEdW6QzRdi5ZN+gpbGpOr8jdYUscOrsPSdf9mtMeGooqqqX6hSsupcYDlESZmlbm7vQ6kOLuDxUH/tZh2/ZbeSOT7x94xaIsqkEqj5e6qqnDiO0m/TYe6WycAUrKHKeq2uHtPqidf34Nr7n8TUl7dUrI4kvKzL7jiJEY7Ddt+Uo5YSx38lhBxPCDnB+fscAHMJIccBmOv8DcMw3gvgZADvA/BlAFMMw0g5eW4G8FMAxzn/fdl5/iMAXYSQYwFcC+CKKnxPXYA7VZUnMWujiUE4xqqbl55SE4kTjiPRvVPvW6rFtRKqwkSeiCSOiTSHD8FgeIeC6iHNmgtJn5MxJY4BVVWmt1jn30QPF4X+iME40mkj92Cv9SHqjKMlnCAq6qwk8FfgXQ1jVfr+lJJtw7//7SU8smx3omVWDdrhVSp/yWbVoMxY4K3dgNdxzQbVel2WAXe8omroJPQvvwT1usqDyh5qwBjW49Gy/XnMaf4DDmt9LPBc2zO6EAlIHGWEo7PM6kfZt/5QT6qq3wBwl/P7LgDfpJ4/QAjJEUJaAWwGcKJhGEcAGE8IWUjsmXB3KI9b1sMAPm/UbQTkZCH/yOpIHBNF4GCM05bqSxn0LiHl96/4wqhSvt3eSqqZAGIi3rM9oMdbeBmv/Lw0Ytof0fOUdXBGHTdRT+KuN41sps53abWHp45X/xJH3Sa+UVSZNu3rq3UT4qEOvaqyriFcGX1Y8YZbaox5mLAKZVKI7BOCRek6GuLtrkkeYcH9vDJ3qMA5wXO0pV+xdo64mNDfCgA4fNAOOcO25x0G8Pq+vtZGPaFWhCMB8JxhGEsNw/ip8+wwQsheAHD+fbPz/EgAtA/rXc6zI53f4eeBPISQIoAeAG+qwHdUHz27gSsmAR0bqlhpHFVV6g9tKkQgcYyzmBNc/1LD6gQrf3xlG95z3kwUSwlfgIRqM66aRmUg9qrq/fKfJeQ4Iq66sopTFRYsVeKX+S7enFFZG7E8Auqk5as8aNebJGhb1db9A5h0zgxsbAsSRGpxHOkyw2rAtSNUouyHJMuugwuUZQHP/hHo2qaRqQ7aHQCPqZKcGqTyUVtrZjAHERtHlWZGPLHyJJFhqK+WsruLaX/utMJgPU22rmrA3Sd8ExnFdmjp6iagqirxP+AyBM262z/qB7UKx/EpQsgewzDeDGC2YRjrBWl5upMinUolfUuHaP0pABx99NHo7u4Wt7oG6OsLXm6alk/H6KEu5BbcjKHPXRBJn8vacX6IZQW+J9Xfh3EAiqUS+hnfWXKIk0Ih7+WbALsje3p6MMFJJ+ujUsmOfdPf34/ublujeHCg326TJP9E59+enh4QMtZ7nu7vh/vX0MCA8ji5G0D/wCAMRh63vu7ubu83jWKxSNVll9XX24f0aHn9rnOUfL4gbW9LLocW6m83/VVztmKoYGFvxwGMa2EvVV67AXcM0rBCF9zunm6gqRioeyibRa67GwcGbZs+A/KxpvtPBjftUDbnpQ+3vaenGwViAqW8966vtxfNYzjlU+lcZIeGAu0Znc+jSaOdYbRQBIfOd/b29GKc87unpwekkA201R2RQae9zUNDGOU+G7SfjbcsmAB6e3thGfK6hwYYNsQk2O5iwR/f8DfxxnOwt8f7Fl4f+Hm7kGtuijyn97FK7LOyudjf1wvAvlQ++prNGX9g4Rb86nOTvDS9vb1wDev5c7QH2bTNbx0cHAy960WeZCJ5CgXxHsDqI1309/VHnnV3dyOXzQIAstmsty+470Sgv6FYstdAsVhknh3lQHUPSbWtxLjFt6C4bRH6v/eEUj4j659bQ876ymaHkOXkcfsqsl+G5oLu/HXXzsDAAJNIzA5lmeuwp7cHRiHjqb3nsllm3cZAFyYAsEBQLNpjVQicXYw2WRZSsPfXUkvy+7xqH4XTFws5HEK97+7p5sbTKzjz2aL26D88uBRn5O3n+XyO2Y7M4ADGAMgX1M+GgX5/fRGi/n1pZyrlqLaUnPYOOXu/uy4H+vvRV3DuaZYVWGve+Tnkx3EMt8G9r/X29iA9NITRAHL5vBdSQmfNExDteZ53zpZSqYTu7m5Yzp2yp7cX3amorwBv7Ls6AVONFBkY8MdBt33e2dzbg+4m9jsA6O/rw2EAQPWBaF7z3o2zSkgx3pWzz9cLakI4EkL2OP+2G4bxGIATAbQZhnEEIWSvo4ba7iTfBeBoKvtRAPY4z49iPKfz7DIMIw17TR1gtONWALcCwAknnEAmTmRdw2uPQLtG2dfL5qYmNDPa29xikyCmaQbz9dmkVzqVAus7Uyn7MpTJZCLvJ4wf5/2W9VEqZU+pMWPHeGk7Ro8BYG9qKn08Yfx4gE63f7T3s6WlRakMuz7itGWsMI/9zkCYt5BOp718+52r9ljqu0TYb9pbRlNTtD8jaA5GC3LTu2pNEyZOQF+2iFmr9+H0Tx8jrdsdg7HOd5tmULFg4oQJQPO4QN2jWlowauJEFFI5r27VftZZNy3Nzdz048aPw7iJE4FiznsmHDsqnYtRo0YF0zdRREyM9Z2jpEg6+ceM8RkfE8aPx4SxowLvXcLNm8+j/PejRjnPnHEbH14PHIwaNSryzDBIoN3pTJNTvz3XWd8UfmbmurjvwpgwYTxGt7REno8b6/dHJfdZXtnd++09iMBAi9O+5tBeYhX8SxmvnAkTJqAlY6/tA6OD/T1u3DiMZeRLM/ZUFsaNGxe7b8Z0ufPU38MmTpzonQctLS1Ip9OBd6ownb2M3g+ThrTcAXv80mYwrTDfkN8XTc12P6Sbmrh5WlrsvdAMBVsMp9ftA5esGTNqNFKpKHOnpYW9J44fNx4TxzZ7+zd370zZBK9pGEil7LHKyMbKKXPcuLFKewug9926feSmL+SCAe4nTBiP0WPGM/NknPlsUiK7B5ftw+lNAEygibfunPtIUyaj3N7cmDHeb52zsd+5VtD3gJSzntyzyl2XY8aOxTjnXE6H729u06k9h9eG8ePHB+6JsvQsGFD/RhcZ52xx75juXBw3bjwmThzDzTdx3FggEz0zWBg7Rv790jLGjRfmHTfOva8SrbUfeUfdu8rdQ+oNVVdVNQxjjGEY49zfAL4IYDWAJwGc5iQ7DcATzu8nAZzseEo9BrYTnFcdddY+wzA+4dgv/jCUxy3rOwCeJ7U2sqkWuDGNHFTYq2oTyeJz5vJAA/R7nm/coeccx82enAKXtlpRQponp9/xGi56ei3aerPMNCx7K+5XC/rQVbE0K2TkKBo/711QH7Ai7VBFXFXVgE2MsAxGf5AoEaBYq0KKGHEcR4JzHCcep9BiVrWNG2cB+cEqeJIqH8l0e4W+s9Jzgip/xa4eAMDKnT2VrZMB/xwiHLsvrpWj+LWXjLX3D0/7svAaFKn5+0dU5ddhsF1xHIwpp9QuW1hZFffdI7qXMZ9L91VLw3N5Ap8jC+nmOmAfniuoOqiFxPEwAI850pQ0gPsIITMNw3gNwIOGYfwIwA4A3wUAQsgawzAeBLAWQBHAmYR4Ubl/DuBOAKMAPOv8BwBTAdxjGMZm2JLGk6vxYVUF52Jfoeu+cspf9P0dn2t6Act7/h0IKJ2UQ8ARzm8xPK+qMWtltkQxPl/SA9Ez5KiBcA5SmhAg0u+WE46rHzFhAAAgAElEQVSV2zVFRo5Ro3ThoVONQzFmHQEbRxVimVrP0eRqbWBXYzD/1BpeHRqT+6LGRBYjjmOsKd62Frjvv4APfQ/kyK8H3/EuJFXwy+bbJoftvYJ2R3FQMfq4ioRjruiYDgjsxSvPWybscBxcujH4YoijsknPO//Mq4B9WU2g0L6IjaMNrrdaxcXQ3pdFczqFCaMywf6MsZaUPOcS+N9S1p4R775ULt6+/wUA0e6RtqCkE/Kq/O8hBGjvzWJUUwrjWjLR92DvpfoYuaRn1QlHQshWAB9iPO8E8HlOnskAJjOeLwHwfsbzLBzCc8SCf9pophdUAUeBk4id2NN4S8l23W4WBiLvlBeiwLNazQPCJ55QDb7BuTr8bhPEcQzBJUzNCu15wsuZF45D9SurQTjGlThS4TgE36MkKU6wP+IxbzQkjpz+qrWDFX8Mov5so2n4MHK2rSQObAXewibSInmqeDnnjW45EqjKxa7VKbi8eVsvVzh2O9j9EF5L9yzcjk9/TTV/deI4uvagYfXeuIg4xxEnlqQq7y504uS5GNeSxqoLvpQAgc3YFz0erRF9mBRqwRhwmZPunUUqcVQPeZWE8z6DWDjx0rk4YkILFv4pSnLwmHAN+KincBwNJALZwtL3JKYTn8x181+eemjkShdpU+JQ5PApSxwTwJl4ENtaTgGKeWlvshWg5J21pycb+JcwD7PqwPdKSY23aO7pqC3HvP0aJGYcR8U1w/qE6LglP+n1VFUrFbqjAuDV74XjEGRVIrz9empNDNOoZEuselZVVXPTXH5ZCYAQwg7HwZ2zivtAnHb7t3r1PIy0H7zwOfzbNfP06+dWwWcaM1KH/nL71lXHL/+s7ssWI+3Sudt4asq6Q8QNx6FQdyBv7fco6bfrqKpysGZPDyadMwPzN+2XpnWP5r09bLOf5DQPat/3lUKDcByukBA60SkbfxKXtOwKXVWZKGefxFW/iBnHMdCWu74GPHuOMLVuWzzc8i/AvCv0i7v/FOCJX3Jfn4pnANhOO9wLB+/7WYSASmiEHQdsyfD2Tvtf5TiOA52SBLyq+ePnEYm0tC78XeufASYfAeSjEu1KIJE4joILLCuEQ/S+qEqEstKFn3Hit62YrlSHtA2859UiKDn1EM/GUaSqGsp786e1qtJhsCUNn+HDbsOEwR04qfXG8ipJXOW20oQjfdlXKqzc1kRw4uQ5AdVJHQtHSiQlhre/aKhhx1mPjDz9uSK2dTK8OUuwvz+H4859RqFKgbYGyyYe9BpPbjy1mERd24ALJgBta/TbkvQ+WQNGXiVVVd0xD5e5eKvt+3LOujZpGdKzyFlP2hLHRbcwyxmJaBCOIw2yRRHj8JfFvQkU7xJr5VwyBJzWOM5xAANofQlYfHP8NjHa4mHfSmDepZzkgvZumAEsv0evdoXPj6SJ7OT8QlzbPOno7VwkbwgLIsIRjItAOP3ci4DCoBPPTYVQcouJG8cxnsSxZMlUVQW6x3HVYzVUVSO98czvmen/9twGvLot4pCaX76y9KRSiNbTNZDH5c+sdd4agjaG/m5bFUny6LLdVPow5ajTzuri2PZZsfNalRq7RC5W6hKp8srio7M/h1/dvxwDuagNYntfLkg6MLYhniqz7yzMScdtAYtNrPotOt+c3Dx4tfUACiWWukWc9lR+z9FifK172v53+TSttgRIomFo4+jCCP0r3T+09oHyv0emDRRbVXXmH8MF6eUfRmgQjiMMHncz8iL+JNbhpPuHYIKqqjG9hHlEbIKcn1pIFSxCpJpFukFNRXUBtfKqGlVVTR/YJCosoVbxYca2caR/E4W2CuxcVL+TkS4yioaeV9Ubnt+Mi55a4/3dm5Vwh7nro0rcV0Yf3PziFux0JOr028gUp/O2rQELjyzbRSXnM7jkT5NFcg4dqogqqqoq7WYxm3PdnE14csWewNywmxadH2+2OjQqDj7njq2IMcVDeUbzZac1OJVHNVXljIGwtN0/93iHZRzP8vpZgvbUVVSbDhw+wVcdfTncPr9VnRDO9QMvXyO1QxSZ8MirUv9Wrh25xpDKanPveOXegEpVNGuqNhqE43AFT2VR64KqBpn74mDp7uYdrSfuQiwpOhrhQyGPqo0jLCDXB2ydJytQqTwVBAhHnU02Rl9Z/qlbISgcMFS7D3/6B4KyGIRSwsTktoNOAgC0Nx0tSRlEsO9Fl1sB113q/KH6+OsTbIJKihpKHAHa9to/8oRNuvmTCnWFHXnUzzglicoNnaTgTbOBQtgOKZinPydgZGirqiYLlrneZUMXSdNFXsj1TqOPBHn6c0UM5V1CQL1n2nuHsHxHlzwhICccDYND2BHBX6Ey4u6PZd4h5DaOrD2dnzpw9Uh8sQXL++V9y3DR02uxqb1fLfucv9paPuueFNdi0Uya0BhKKbXkGBIqdx6ZICERJtyBVqR6tsfPX+doEI4jDkGum/y5HKWECEdlhBZ/sSiweRMX5BSX4GZMCPDIT4C7vwH07lVoQQJcJ8uP/8WVOFLOXwbyjkE/t1H8/vC9qlbKrargVez4hT6SlrgMpe1AvXub3x58YZWALv7BEGC2CCSOrEMsklJx/rKIZt46jNqh8Oug+7R7MC9sA2++V4r4yBZK6BmkiAdORabC/qfSxsD8CttW1TKuI4/GcHkQCe7HiUFU7u6lwL3fAZ47V1jE7x56XVRB5ImoF1TPFkKIZxMuTMd4Mp70KaVkPR9rDPEaJG0LjbPvX469PZyyBPjK9S/hW1NeUUwtbpNpcLRkwvuh0MbREqZJ8iyItQToM5S5NzNriuYNlKlSMX+PcsN6FVlqwiz0tzPLCSNwRwy1vT9XRNeA4NyIcb8sB3JCNoG6bjg+ft5hgAbhOFzB2Vh4i2J/n8253dWtf2BAdZOh22EkN7WKJVriqLPJeJmkaVXvfIQA6FjnNIzfl+V5lXXgFEFLHFXsjVbv7nUbwQFflcWzcdRR/dA4VYUptcJxGJzDONlLrm9nFCr3hUuB6z/IJR4DdhTE+1/wEatcdisU0rBTRQ4/jv1GtsBfV8EYoZI2VJl4+u9bF+FDFz1HtyCSxgDb23N4jqvsLSLhQD3LG8syOU+uGaGCBf096Ei2DmwVFrGljUWI8cuP+y30Hnf3wu347FXz8PrO7lCaYB5121AeUyn4/KrMrbzGqRYJANhA95nG3t0nU1MP1C8Jss6ROCpqfwuTeGucz2WVFxopky5Lkp9Zr+K9JXH17TLLKzoS/8xoYTKLocrqOvT7r38sxIcvns3PnMA3a42oTGo5HNX+q4wG4ThcwddvARBdSJ0DOQBA3xAniLAAOl5V/QtatE1xMebx06ii1MsyNDYAVeKHhCVJVYBFxdHk3cuZh3CMfnc1TnQ2Yh1VFLGHUV0iipHeCs+8csfInUOhdre+aP/bz/biRtfLaoPbv39/YbPzQMCdVp6brKdG6C/2migKbFh0NKl4/V0pr6orQpd3qcRRcGlUY1fwufm1JB3dmitx2VFZQ398eCUmnTNDu+RyIfxe3Tknsr+m9pWl222i1pU68qZUtDgCpi9f7lEen/CUMS7jeB7VmVuXzRCrtBvc8tTXlLsnhzUtfKYc56yJsxfRy16D8PTWpQoBTHh/lAGeNFbxE7bscWxyMy3iaqj1obsHdfSpCzPk549CGZJUrtqtWcP9vN7RIBxHGHgLyyrZF8NYduHlOpdJ8gCPQTgq6b2r2jgS9uHPz6BUqOS95XHvEvFwKCjDlzgmqKpK+PYPwWZxpHv8DNFHnKRx4zgipvosfZASi/APcETnp6rTlXhwx7ec3IL3EoZW5cHrZ+K9Lf+i7iYP2ThymTrDGyrdMn3JzmQLVu40v4ySRfCJS+fiidd3R96VjxhMOOf7TGrvYZYi2BuU4M5DalEr73cVYujc+Uqr8L1p8lRVI08EpbhrOkw4cjxHa4PgD+kHgK5tXBV8Ubvkz2wE9+IkxsMvY3GrPK6hCL19jnQ6LSYcS6V43scBYPcBRXtLQEAIq4+2PMZy8mvi/01ZgJtcJvEIQINwHGHgXcxdD0/y9RVNoBOOw5U4JqouQUOLcHSz1KD+BAkvq0RLHNW/hZ+UX0bJu+woVyMf3YDEUUQ4unMnPqMiWn5548AlZiXjEHQoRSAiaMLnWIQjqjzmydqKsNySx15L1YppxZU4smLdBeeGmsRRlIMjbVUot1y4n10JiaNr95wt8sfwJHMNfpt+UKvcO14RqKFGlrHcVncgX8S+3izOe3y1U0a0vcLdQKQNoeR0QybJIGxJoIb9MyehWroYyfdRQdJ15tZHTIE3bNhrj62qqrH3SRmN4ueyr3mnsQu/SD8JPPjD2DaO2ncQbyEnc3/YEzJN0v2OFti2ibJsVoA5HPw3WfDWis69SOIcpwLMlGU7unHVrA2Jl1srNAjHYQeZCgp7U3Q5Qr3ZovbC0CEcvZrL8oSajMRRpTztkkhJbVMnkR9l1Em8YS9xuGUfN9d7v8dhEKelZsWyN/P7V0MdR9XaPPI7UpA8jQvD4KQLPqPtY+NA/m08W2Oa2GK1wc8XZgaEL1Tr9/XihfXtknZA6Vbglx2uk8LCKUBhyCNoeRdGQgjuXNCKA7TjgypKHNft7VWrx6Cd49Au8sOSXgUbR0Mwl6ukup44dr4GbJ7j/blyV1D9d2eXffncLPDEeH/TZJyVflyr2muf26iQKoYHS++V/27Vrh6lNvGLoh20qUGVySc47dQq6lSUZFglYOFNaCI55Tr++uRq77cO4Xh/02The8NQU1UV7b+ymcFr716HGN7dFVWR7BkqeOEqMnCkaJalt7aZNqfRZ91DBdyzaDu7aJ5kTYWZQOXl9UH4CuM6zQkj7fRBoSiWKFpWlHBUho7fCm6/lPCj1DNIWznm+0B1CbbnjYoG4TjsIJn2nNelkn9xWrC5U6tGlQPwxY0dKFCSscDGR6KXNiFUuIwK0FFVZfXbxOyO2NV713NCgL59wJ7lahmZhZV8iaPCnnZx5nZcmLkLEzteZb5/ZXNHpE/c8l06S0fiKEWAiBIlk3GQwZxXwffBDmrf8rqTNK5upit1C3e8RLoQbmaorR82Njjlksj6IgRA7x6g144L9+iDd+LHdy7SbjoAFMKEs8r8nfUn4IXJTCYF3dS1e3txwVNr8dsHX2e+5+VLCl+5/mWcZK5BM+SEqylR3evLFvD6jm5hGiB0EWM4gJSrQVUGZdnyTv13YNq3vT+/fuOCBFokh5AQ6Q3GROSrczLmKCPd0QMrpe1R2puYbVAtz2KmHde91j4jdBrkomMDMP37aq167i/ArD/jJ6X7letIm/4VUbSDvrC+PdpHJb4zHZ7Xbq15HDIjcO8XAec4hKB96VPYtNdf23sdKVxHX5TI+Mvjq3HR02uxaOsBX0vBTMVcXQHr8Mjbi2duwXmPr8aaPb3CdDSO3vOsvNpcL7BjYaQFduns8s9/YjXzuYsCTxV156vA4IGy9j0dhoTLwGkxCsCOxd7zY/c+hfMy0/CZtruVy+C/V27OGxYNwnGEgbcxlCjnF4N5PQc5sk1h8dZO/On2Z3DtcxsojlBYVS8ZxPGqKgpO6yeOHmTv2f8ccmFOG23jKNxhqPJu/Bhw6+fkbeDAsoiWjePBsO0SjBLbBfaV0+fgeY4EK5ZXVZ0USvar5cyXYN4jh8pVD3GZD3q5rICEgkTa5cLwyg5dMv72Hu+vP2fux9npR+QtVbD59J3jSJDt8W20qFLo+Zd3VBe7qHAYXFugCpzG7zR24v6mybg880+6ImbacHBw+5nfC7+8bzkuekovRmX4Wwks3L6gVauMpMBTVfVXVHxOUKW8CwrLfepsJ1HIiYto/4jwdvwHX0m9pt9Auiia+WVZeAvktmOsvZplS3/s6uuAG09kVSpv2E2MfDwsmgIAoZAgEsIxRat289P+752v4ckVe4IPRcQ2R+IY1VQVEezOXha2NaZ/bXgGb37qVDx84zkAbA0UUUih7oEcjkAn8iXLk7bBTIXaoS4Fl3p4BbyYmgRETsgXByV1A5h+KrD6EaelvP0w+A3hcBnhXIUCg3AkBJj6BeCeb4GwnKspbjmxYy/f/kWvvzJOvzQX5WFyQIBRyOJgsDRWnAQC5IsW9vfLJZsjGQ3Ccdgh3gWgRKsSaOrPy4i1/K4VeKXlV3jblmn+RmUFD1o9qBAXGqWp2Kdwnp9577JQOrVv8VWGic0BLAOEkjiqeLhNOW20nJAo4dF+vPl8lLazJVgukyB8sIjbp5NAYWzLIDISp0+cuR89gCUq45baNxsg0TYzkh9jMCQSseCOr/3v6t09gb9puBffFkqiR7fV3UcCOauoqjqzyb4Mvt/YJq2fFY6DRpDrrwLGJY+I1TlriXIUCCrlXVCJIPX2BHkZEUItQRMJuqxP9TyFV1p+hYndQUaDdBmL2pNjqNJWSPSR0mC+ZlL+FVE2D/ZS9pA2+OlNTjiOiIdUJW0h9t5sgAB9dqzlow2bUXrZs+vx6LKQNJvCV/sexMKWszCqZzMlcUzrETdMO0V+/hLrfCzHxnGoy/upyvT5ct/DzOdu7kKJIWxw59He1wPChTCvR44yHA/5HDNlWMQ+O5a1nMGuQTLWv5n+Ok64ZE5FbCGHCxqE40gD51DQs1MMoiTJO2bAjmV37NBKaqPSs3G8kOb2C3WG+K8efG0nJp0zA50ON8hrSxk663PWhSRzIYKA54Le5zTGrtovy/LjOKr0palw+EzoC9rFEE+iKc0aAxQTQUkNNZgmy+J2QqA+kyS4xKx4HAKXAYmNjJan3OXTgAsmAIMHou9iqHE/t5YdTgTw7WkDhCP13SK1dF59ScJVP22COL6cASNg43hI/3psazkFb+lbGUqnC705UUm4NVeinw2q75ItVwOcPZwnDXcyabZIwNSizrNjh+x5M66/FQCfEVv+vVKzAEUGXYr6lsGceO1kFCWO7PY49VwwAXj2nMArA5zx1xlDUhK3izEAL2xoF37H+7K2SUlT/x6k3X4yUsEtTnnm+umeW7MP8za0Y9I5M4JxNOHvs4QAfVmbQDswwJeK6iCiqsr5dPe7w/lck5VCQUA4IuwMTg968ypMOOqZkNhZLLzN5PsMkAlKZqza66STVjVi0SAcRxo4k9klHAlDlrSprQ8iHyIyJytG4HfUq6qKeukdC7ZJ0wBiid+9i20CdseBQS81kFAIC7fEwAliQb5RlV83IZY3aq9s7sS3piyI2q5ROCm1FgBgIaVdl+9VVXw49uf8g0Rql6JAWATehdL0DNg2KQcG8mh1YqeBEOQkh1m5eH59G3Z12XOJe7jx+omWuNtGjuzsYNk4Cr5hoa1uht49kVesGo4wggSmsqoqIZ49bYvBljiy6uXKGyvob6DJoOaiAoHxtgOvAADe0fVyII3KBUYUx5FEHyWO/73jVVw3x3Yqs3JXN46/6LnELpk86Nkg6TMvxInE4osTS/6FN6KNoTvphGqw/ruP9c0NZWPni3oIBbScjmmeHf3ZPDa390nTfcJaineY9uVXpLYJBG0c3THgf6/gweKbA68y+R4sav5ltAyOqjULvvdO9jgbCqqfPFgESBmUqmqZ4TgMEMxcLdYaIQA6+mypLW9NB8P9qnwb59yhyunPFSP1+YwoGwWWR2WacKRUVY3IDzH0pLn8NsQtIppArT068c1HGhqE47BDzI2QIjToTaNnqIAvXPsSNtEHzktXAe2+l04dwisihbAsHL3sKs3WKhAXB1qB5ycHCVS3DeHLPGtzCX0Tjw5rRh6Y+SeMwZBTh+V3ILGi6jtL7gC2zaeqib+5jHPrJJZX5TWzN2L5jm6mR7gojMA/NHitUh3rjRTXVJpliCJeRJczWHji9d14euXuwHNrwLYnenFjO0rOQFlWCUMMwpHX38K4Zm1rgZeviTw+97HVvm0UsYAtzwOv38cvh26zxrhH+DKMvN4QFp1xZ8bV0p9rorM9vfRWfNTYgIvSdzBr8KXgVAuqpaq6dZ73k5Y4suo/bHAjfp5+0mmFgXX7er3fND6XWiGtVhSaRMmL8bqngDVs76NNy28Hdi0VZn9hQweum2OHOrjlxS3oHixg4ZZOpLIHcH76bowy2BdOnaDloZzUBV0OHR8ZWiqwnAvir3P/8JMorCMmFv/DdvKhPUcl6uosulEHrPY/fwnQtY2T3sKpxcekbRsP2g6MAPOvAxbfykwbtHG0oTzGgkv9hLZXMN6I2utF1lAMRqM/Y3nMOjksQigbx3SwCtW1FFJVpfuSBUKIR0SNJ73ArHMBloqol16hCZw+oPOuZ3qn9goAwNFYE3nO79yC03L3Q23WM8pe8QCwyfH0vGc5sPAmt6ZwI+hmKqGccBxXzlS4Fx8QhBkaIWgQjiMNnMlcctQ6wt4lw45yjmmbZR9Od3yZ8gEj4eoY9E+3fuffvcsxvkN8GYpAsHCXbjtgO+W4/2TgpSuBrtZINtvhiH98zGM5gqHqGMgVueonJ6deABZNwSjY6q8TN1BOSohFxYdz8PSvgTu/6pWn5D5bAlb/W4RIiVIrxvJ2bRVMSdZA1dkem2Dmted5sWt2r0yL4OwHXsdlM9YG21SwCYPmzvU41rQlbds6B5DLR+tbuZPtGfNDxhZ+xbf9OzD3osghHfmce74FPP5zzssgDMqJASFEeImyw60oOlwoOkb5qTQ3SQS0fTNxD1rnX161y6dhzNw/45HmC3GMaauz5kgmUAfLDnYwX8DU+a0Rh1plef1k4fEzvZ/NFOHIcuT1v6t+gE+Y6+z3MHCgn01cXZqZKq02QOzEYQpNPxV46DTmq9EvXgjc9m/8vNkefD81B+GBJiB4y+YHcHp6Jn6ceka/TQIYIFoSR/eCub1zAE+8vpubLiyFX7mrG0sXzbOZM+wM0ro9L8BeUsV2P/sH28kHD0NdwrIMw8DHjXU4pGdV4DlLdVZvxjBSv3QV8MCpzNTuGaWNOX8Fnv0981VTivaqardHmSkm3PPUrvriM87dywRnLHODE4yl8+7wHTPw36kX7IdmWpgnWjyb8UfbizKzuQkBvNnqABbeCGxgm8IA8VSz3RQWIcC+1cCm2dI8AFCyJKqqtGACAKb9P5yWfwCHoSuaT1COh8d+BtzreHq+9XPArD87acOMBZ5jJFF9ktfhOihCcMq8LVQ6TgH3/Te/8GwvMuselTSw/tEgHIcdJFwrzqrgSRzDl7/Duh3Vn6J/CMWJ4+h52VI4ZHqzYjsLGmv39uDmeVuAYlZY/urdvd4hMHc9344LsFvMdpTuxzFy8aa1dwL9HfYfVknONafaFw7GqwqrZEWkqBaBNKwKMWIQjh7x7dRXzAPd4rAkmafPsgnmPcsi6ex2qDkJ8C4CoaEoOW7d/2P+t6mkFrIMieP8zR3Mkn+QnsN8DsCX4gmJfL0r39FLrwhm5ZRtq6qGa2JdPJxnBVFb2XWQYnTehYmB6OyPtiGLTKBtnsSRenb9nE24+Om1mL0uvOYSJhwHfGYQLXGUX2oppxwxpHD0eo+OU4Xjf834LSZnbscJRtRTcL7lEADAJMeJEiEEfYF9VfFbe3bjHYZP8DHcH0Ux4O9DriO0//z7fJz9wOu8HCAk2KKv37gAH535DZs5E4Cv3SFD2c5xeMmf+CVbmmzYjIplO7owvflifO3VH4iLI1EptxA80V4x7ITGRoqnVdG9A+hkM85kzEeWV1W6nw1YOMlcA2bnEb5tN68fdEhrtz0mh3iI7a0TwFHbH8VXU3Y4q1LIxrEvW8Skc2Zg2qLt0ha6e4wBIiccCaNXwvOeaoeK5Jc32wgBcMungHu/4xQbTBnWMmA6OKTUU2kbR8Mg3v2RyXQq5oG8z1jVGycx4ahWgmSfCP9986e9nx80tmAs7LbzYmrz1icA4KmzMWbWb4A9/L1xOKBBOA47qBMqNHwbx3Bp/PI82aEi4UjgX6x8xzTyTeG//xH28CniCMK5ELGuuj7hkS2WqLbwWus2kehJ51zuG7EUuPH++09ezuGmM7PR5UYPFEII12kMPxevfB8li+AQ9PiX8ad/A1z3ASAXtJ0JXNIGHGKtyFGTo9UqRaqq3rtgGqsYZSwYhDAJx7Lcd5BgfwaIpHC7JURHZsgnYAmswCEbKAZRG0fhlJIwTFggefogC64JHe+5YzGEjOWXxdIUc22mcmGbmKTjG1KVNhnUBUayVxGEvDn2tweYZDIEpBsR29QKSFZp9NvEcosRXQ+EuqC6+Pk0NiNHiGvfi7nNvvTpLUannDlGhRpybZ1cJx88WERPkslbFEHV4XCWZAj5fN9+sGzcUqU8pr+4DCt3MTyigi0RSsa9kOYcu+4DwN8/wnwlGwNWHEf6s05NzcH9TZPxZfM1xoVcQDhymJrRu4bgLuB5vA4TjnxtH9spD4vI5dczVCSBdrh722XPrOPkYDP+MqkwcWbhcHR6/9pC+HBe/oyJzq9oWt7alTHYIiwxmapq5L3dlsMNhsTxn/8GXHoElVJjPiewxmUMpggzpeCrdj/ZfB6mNl3NLkcFrm+CgkJYlTpGg3AcYfC4baHnPM+o3PsctShkcRyDdTkSR+aiYm+C60Q69pESCJoznEMnJC1z1UjZ8aJoyQH/eGIe9ZSNI9eGwPsR8/JCERrEsiJ0ikXk6qRicNptlbCk5ef4U+4G+8FmR5UlH4yPFLwkiOeHlR6llNYdk3Cflph2HhayhYQlPCHijhAgT1wHQ3rSjMClxSLCeaBmq+LAIxyj5fGkBxZ1SEVUySPP+UgZBH/t+pNfLqM+T9U5yjqXlp8EZN79IuyYq48DHvg+VNsnlDhWyVmCv8cbkYd0t6/a3VOWjTUAzG8+Gz9LPy1O1ONrJIQvkFxHKohr48ift1EJgN63uyYBblw9F8t3dmNbRzQ+3IeWnYvvvShQLY5MD6JHOpYxdsr1SOqgnaSxJI6TDFuz4EiDEdeSkAgzzhmg3ocAACAASURBVG+f4uElaJ5J7HOBL7GS3Vuo94J+KBCT+bpQkoxPoO/CjoaAX6cfxaKWszA5fTsWtZyFlr4d0TYbYWJTqclUejZRROcV8UA9u1YJ4ci7Xz7efH40XmJbUKVb+Y4UUm8P5tVwOiXdJ8TtOd4xeykjUMGwR4NwHHYQLxB3o2jvC3LRrRLt9covg+QHsa3lFJyWDuu605xc1YUNmOHLv7KaIp1MwGUEQXNa7C00vBGGN8+/PL4Kx57r2wJZluaBTrWTe/khfhoZ2noZqg3UgUs7x3FhESKNx0k4hzbdPBfu4ZvO2heAk0qOUxiXMxxWByKMPzjtKbUcRCVVIRxD+QtRSSaxLKZX1XjhCFxGAL+/+OVyxoAePxDuPHDtcff1UOqkovXmvtNgSFgFWlXVbsd/phZjZtMflctw8a6C7xygxLiElDzCMcxOqhbhKOOkG/htJhSzbLOanQ8QJHYiMefA8lmdJARrx5WyOOqKBxv9OB2PcdNj/nV2mAQFaevHzfXSNC6sEPOFxXP8f1MW4F+ueEFT0iAnDsqP42hjkKHJsadH0czg1n+l2hN+qUnIeh7KtbIBiLsPUjiwFdi2IMAAkjnHiXY3n2HG2710mDEu4ZgtBCXw3lrgSqn1ULBMBMKLOSUUeJSDW+3L1yDtErcMieNnTdsZ17dTtnfn5kGW19WIrpH3a9SlBwfesLx88hnb4b2Lp6rqMAtYGjN0fWGvqtT+78bQ5EF5rlol8AlHdUTus+F9XCIoaTYK2NZyikC7pZJnQH2gQTgOB0z9IrBGcAkYPADc8GGgbQ1OeP0vAIAPm5uAG0/0JEXupvLJ1FqkCz4HyBpkOxOhIePisySOsCxg91Lgts97b44rbopm3r8ZLzb9GoeAVveRLNw0e9q+p7AWs5t+D6M4BEJ8iePUpqDHzGmLgjZ7FuFzaM/P3MN46hMatzexPca6X5AmOcbTIFo7GC7UQxLHN1kH8GTTuTjK2YQtosCzjXFxahq0yz9gOMSeYRPpt764Cb990Pc6GQxn6ctAWCiNPtz7PWPVXttGlQGDIyF++zMn2/ELA2nBVFW9uel6YPtC+0I85ZPMergISxxBEwpUm7YvpOw5OZcTiw4Rwee8uzaOW/aHnOnIcNOJwMB+4MaP2R4h967A8S//jJnUXDndttPoD9p/vtvcifG5vfK6OHCliyTwzMLl6Vtx3LopwcTlSL5KBaCDsut76H8Bi20XHZB4rZgO3PmfwfeBOap/wNMx8MIXkIoLHJ0KfuI4wHlLbgteaPoNMnm2quT/kie8371Docv1/GvtH/moJE2IV//pSGjZiFwwZ/3Z9g5JYdmObuzrzWoSjnFsHOOF4xiDKCNPua2UnXf5Euno+koagb2G9qx6w4eBO/8D18ze6D3yiIhSwSaQN/t24+dlpmFMLkQgkJCK/vQfANcfD7Sv50ocI56KBW13CUd3j242inik6a/M3Kem53pnCDsuqbrE8d9Sr+O6zI24NR31xB1GM8k6dfr4hLkWjzadjwyKTrvttXnCvFNhFvqF5X141cXcdylLEpanfR0+UFyDWU1/ACkI7PDAkMFJJI70fWDZ9i7QX3x+5h7Ma/oNcPOnAEa9TKKU2ahSdA2F/v7k/oeBp34tLObI9beHC/Z+/Sb9UPDbKTX8MFqeP5f7Toaz7l+OJdsYsZiHCRqEY72DWMDOxcBD/8NPs+V5m0P4kk/EHGr0Avs3APtWOcX4i3Ni+6veb7G2heMaQePA8w9YC5hzgTzDwhvxNrMdX069Kk/rtKg5bYIVC+DHg7fhOHM3RnWtx4LN+7nSwBOM9fhFyr9UWYRwneOw4R6gJc9bYxhjirZu/yGDrVTbOf3I8Fg2c5XvoIIQgm9kn8AHzVZ823zZeQaYkGy4HrHC+DZOU0xHnz9rOHaJjsTx7le24pFlu6g20bZe4gtaiZp7qcIArp/JDnvgcvrC/ZTOdQNPnBlOjGye8/2z/gwsmgK0rwk8njJvs7CdrO/w7GTpNTDTD2b9rZvm48d3vRbJZwQIRT7n3bVxDKp2KnKOl90F7N8IzD4fmHc5s3wASM+/2lYP6mpF2H/G+/Y9wS+fgRJ1ZPiBq4PaCSen5+G4tTcolaeE586zCWXXSdMavle6ALf4sZ8C217mMoUMgYSZB7qfDCvshZdwJKvJXv3dsCFfOXAPjjHb8Ka2V5jto7979Z6wOUDMNj3zO2A9X3XVJd4mGXvxndSLSC2eYnuHZEDJNxEjjqMbrxcI7myqEr4DA3lhTL0PIcjgJDBiXZSYggsdh0yuRJ/zPHFwPKtSFdv/DHTaBPJjZwTm2zvbZ4aSW8F9b92Tthf0V27AnPVsJ2bcOhlIhQhHAPiouYkyUUGwr544E1s6QqFI3F+CMyxPjEg7vpl6BV9I8bzFk8ivCcYADMfJ242ZG/ARczPeZ0ad64xuXx58EFFV1WNGBHLPvw5n5W7Fu8xdaOoWeBlnQEo4lvh76UfNTZhktgFtq7GvdU3kvfL9UlXiuPSOwJ/bOwdwydO+p/ZJ60KhZ6j6z04/FuzTPaHxoDBq2T+FnqNF2NuTxRUz1TU56g0NwrHeocKNMR3VTU68wo1tfVi9iy1Z5HqGopsgS8Owg3D/ksMhwuipKFFVTaVMZtl+cHMD18/dxCUcH26+KKCuZlmaznGcfrYEF8/DB21OrXu4AUFpRQAMwvEPD/kblmVZOMiyuVO7yKEA7HFLyeaGxbfxdBF5E5bkOGMb9i4bkOy4845zKbIoG8ULM3fh1eYz2ek4qqosEGLxVYUMg8m0uHJm1BtlqKHBOiwC06O0AiJWvyoQzFnHCvdCqxoT4Tq2CccY6i0WxRhQOXxZB68mAUHPYda2wNsrpHuICDscwmhQ7EUYYNva0NINmpiiQ+ns54ToCIPOY5bU8sR1ibJmTw/mbRCreQHO+mbYB5NgourAGecZTX/G1Zl/SBLHkzie+9hq/zEh3tyKnGWc4k+/8zWcMW0pegYLmLV6byQDa7+Mo/oZvRDrlcFzFqZSjjvn9vWIJUs6bfKd4zhjEZ7/LIkv605iGFi2ky0llzoqoWBaUcIRoPYornlA9JzhMS0BoGCllGn1p1fuQc9QdF84I/0UvrbmbADAIYa6TwfpwnX7l3MWBhwHmb6Jj+6MkkscQ+8559nVM9dGnimbQllF9hyT4Mz7luG2+a2CFGEpt/qaYHqOjh0zd/igQTjWO1S44hw7NBdfvPYl7gHCdjyCwALVCcfhbbyWpbaAnDaPRtTzIydDcCvdsyyyabq5hUHfKezsGtTbzJ0L++4DfDWvTMm2iTEpwpFHyBIGUUFfUA34HmJdiU/RIjAh9lzoHhqHD0UlbbyeIc588HrZmVsu4bh2Ty86+3PBuSbYvF/f2R2xRWQFfqZbFYmNyUxqcQ3yeYftWAwC+xnq0p70OjgOpsCDJjMNXaQVJBx59qaGUzS9VFiXpaMMBofercOIcsSZIKXIxagcWyiLELzP2IYjS7R0nOMMIyEPlyptitQdiD9EEY5Um5qhSjhSTINSyD6QEI4nR0Yf98g51V+9YT7+546oNNurzr/Ke3M3qHxnAoTgg8aWqO2lpxiiL3UVom8vkO3FGENuO6nnHIdXhoXFC2yVSSssoePMuS3ttjrgQL6In09bIq3jKKMDG7aILp5slGty6Wk5lMFzOXXqYuF7GUMneA45/et6zxYEpweAPV0D2LGfYYZhmHibEQzXs7GtDx19Oa1O8pzjhPbgcXDPFx4xZYO+SYiIBcswue069s9+WA5CCH5533I88KpvCkOv/bd2q2lVBRsruUN5Yc/Y33qkQTHbqHOC/pymvh042AiOk0FnATC2e4MfCsoFVWe6f0+44ez2Ms4HVa/9rLz/csXzmCokCoGizImRBrNCiu6dkgQV0haoMhqEY71D6cIVVdv0XwU9jNqP/EVtleQxFIslcRtoZaKgTZg64fiXzL3ytE6Jgb300Z8AT50F3PMtvKtkEwU875w8/N8tAvtRFpw2n/MIW+USAFIOoUUTjtz2MMYgINmxAF8ya398ySKR+FVhGMQCevfisNw2YbpAHoeLe5y11Z5PDpcygxLOSj2KaTddgOf/dlpASrq/370kBsd7X08W37xpAa6fraaSccxDX8TZqUfw3dSL0rSWRVDizUuOq/fVLT8GbjyBUZjzLSseAJb5Nq0G49IEILDOeDOcVoM0BBJHA8QmHKmSWPPkA+Y2frtVJY7EQvjgKoc5munZhhnNf8bUvjO8Z9/NPe79puMI9g3J9xkuNA5yFoFKAn1L/abG6OrMLUrlm5Q0w2TYFH2y8xG1hl77XrV0APpzbvif0AXHD6RJhSYIShzf3zkLTzafhy+AE/JIR11XYa5MuP2TwD3fVCwunsSRxhgjh5PmfgfY+iLbOQsDOWff+OTlzwdtVjlVH2Xsx89W/Re3abwA9Kxpqyd9DhHCooI5MHt2CN/LGDq/TPnr2T3bBwcdwoxjZ+ziuzcvwNdvfDn6wjBxSeaOwKNX/34a/vWquVrtc8+qsCbP201bDVlnfomIlxJSfO/AVgmXzLClaK6X1YGcv6bCzJEfpJ6TNESTsHDXL2cdv8ekxt9I+Q6OqHn4gYc/i4+YQeZyuBXHrZ8CPP4L7+/uwTyKFOPgHU9/168GBNkiuz0sh/iiMX6ejsFtRc+vXKGIi59eGz3HBhhefrkInYnlMDmvez/z8VuNNlycvt27ByQVmKdWaBCO9Q4uR5ia7A/+gJu2w7nUZww2d/Bnd/M52i6KEo5QcNG6FxKLvW0TAiy43ue4sxJJVFUjTNLl02w7Twcu51mVo32soamn7mzSKpIx2mCdm56hqkqnJRYdk9L+t2hZMCTG8K0d/XhuKSfWFCEYzBexIhyDjG5LYTAgcfxt5mFcmpmK71rPYuyAfyB19jnS4tDu7RIOOgf4bzKP4AyZ+3/YdpM8iSMvRhgT9JqZeyHw5C+9P/nzh3DTFJzwHWH7OZGntqiNoyLcsVKVODL2h3KCZKdy0Rhdx1m+7cwHLvAvSRc+FbVt0Ye8k9j9zJY40hf+ExU9h8okjkdkWbZD5V0S3v/XWVi7pxe7usOSelpMzV4LhwxtBQC8FUEnSN5XJC1xBGynaApIIo6jh97dDFVVdp4CxXAKahU4xLeipopfBjs9K46jDpKQ0v8HFojrkIz/eyk7PHe8Tp/qlClhOhsGzzwjuh5OTc/FsYVNUec4gi4LO8eJgPOYNb6+xDH6zhKcBClYSDl7ijuvRPP64syd3HfBdnitFab31q/KOqbOxYeXyiRjjNp32MynQsnC8RfNxjWz+Htm5wB7bqRZa0swz0+/k9IIYKiqcu9Ucy/0fsq8zycqceTghszf8YP0HGAvQ7V1GKJBONY5eNyP/lwRk86ZgRc3UipsjLSdDuHYRKk10usibLvGgiWROFIN8DdNAvRmGZvHga22M4/p3/fyhPGQYFMz4Ni0CDYD9/uUVB6hqTIFeP0czjd7bVskaZoi7lKwsHp3D953/ky0UyE4CEPlJ3jgEu9bPJVVi4AIDNIB4Lk1e3Hdc2zCkQC4heXdlCIc33v+TOx3DoBMSC22ZPj2ErILoOo46IBYFlfNSjqagfgR/MtPcO35eegLqmkEv81VJTbCNo7cw5E4oVWoJ6r2gDSRryRxLDGuIfEPSVYzyyFEk8Cirfsx6ZwZaN3PdoJBX4Voib0qaSeyceSpu43KdwJ3fV2TCx7ED29fjF1dQVWxrkGKMcOUOBre36XwUe+pQVaAcFSEUp977XQlvbzCUrCsEm7KXIePYm0wD6dIgL036ZL5PNt1AuC6zI2hJwIUhoBp3/b/9tQQ46+pJoiJO9kZElw5JFRm6MIdUYPnkFycs5uAdWkn9ve3R4kUV+LID7sh6zf/vcgcx4LBnUsmLJgO1y9fLP+cC3/+/gGJyrdE4hgAZeO4aGsMj55Ofvc7Z68Jq6fKkTEZhLnqeccwtXDnV38u9P0SNWqvSEIQXZflnmEs/xtuyf67E7IL9b1a1wkahGO9g7Mh9GTthfGTu5YI07oDTB8g9LLgOmyhUJAcLjQoJVh2gFz3MNyzHP1LpzMPxYeW7Io8o8u3L+78413HyQqgRjyzEO67+yhvf14aQkscCabOb8VAvoSXN/mXyK6BaIywgAqV5R/AKYdQKVoERKIqZB/c/PHNsQ46ihhJo4j9A0GX4S5oNVF/M2f3uMoc04VlWXzHTjJOPU0sCvow4EGTmqftvfyYbiU4hzPFAR7MF7Gvm31AGLAJsFSAE6tKONI2jirprYRtHKP1HknY4T3KjiuniJmOs5PXOK7Oh4pUO2IQTSmKUbBhd5AQ5HGq37vjPqD1RUz/xyWB53PWtuHbN78SSf+Fv72IjW1Bm6PeoWKkDwdcr8KUE5KwD0ZuqBzPqUYtCUcVZocF7FoijzdppoB8H76aehU3wPUuLi+f3ps+tvJ8eXtYVXP2N8uy8M1UcHyFKmrbFgRCXBgWm0DTudg2ywjH0Pj/4eEV2HnAl2zTKo3uNkPfJQJ7JONSz+obhUBSQSy8CZjycXse0OUTmUYLwZ0Lg2dyM/L4gLE1mpIQ7OkeYtrDiSRQKVieY7O8K3HUlFgHWxzMu71zUFi/x9RXlDjqMvYCs9WRWLqS1TR3KvMtRlOMPTdfKODL173E3bM9WNFwHK7zuogTsRX3AX02I190OhIrytTV6aLRjNA9Q4w4sOHC32nuwh+7LwKeOlu9sjpCg3Csd3A2BFdXPE9d4PP56OHqcsOCEiN/ZfCJJpobp7aS2vtyvpSGWCiw6BKKEzT2qZ/i1dYoFz6TijzyYIBIPcESKm0EDPfKmZiEY1gKx9qg6HhJBoi/6VLBgNOMwzUgySL+dcM9iC9+ei1KRfGlwAQREm0mQz/yqDZf5TcNy/M2G94ge/r9y4V/OWCPC4vrvLmd4TRBA8TiO8eRqV8FiEWBxHESaAYGR+IY6l+XcKTn+f9NX44f3h62MbNh2ziGogtKGAJ+ZWoOXTwwOLZ0O7SwYxEOb43aBh+KqPpqrPJjwt2rzIBKalAK5/9RYqYRwU2XL1loOxBU8yacckynntbOIMPhx3cvwdLt0f7a1N6PG+YGnTgxnf4w7DXp+kvE/zsSbsgjHNU485WASp+T/RvsWMDPnSdOaKaj/CKFGyBb4qg3V7kSx/A+pOGd3E7vjGnoO5gMWQ6aDMleEhr/B5fswp8fW+X9HSTyHImjY/YSnlPh7rYJx2hbN7SzYxXa+kqhviQE2OWY03RtC5bP8arqoqM3i82hui7N3IbT0rOdvwIeyfDJy5/HQC66HgixuMRbChZSIYljWYrpoWpumrcVL2/khy4pueOnotYc0BKKATOFVbt6cL2zN5kCApmnpHbQgWWRZ3u7h7B+X5/cnMEqYsGW4H2RZ18MAJht7xkivur0JTuiE1dDRfyWzLWRZyoeuk9zbV0PRJkYwwENwrHewZnErDvz69uirtvdTZVWVaXzTjAUROUScfrobv+S461RwrBFBGANBDfBXV1RD5tpl6DZ8jzQF4y3ZQAoSS4Emf69TlpGOkZA17TEOykPYaKbeQkp+gRXChYOG9qKdxo7kaZ2s1SonOU7uiKBxt0N0n2+tWMAK7aJY2GJCEcSsqvL77c3sGPafLu0/0nP9No2BkGmxI4OP7zLUYa9mRPOJfRdRlT1+Pu3ib39ydCbzeOpFWzbVEtCUAeIxSzHLTyAG63J3m96LjWn/W0zfGkpejahfr9n8t0RL4KB9hIExuKEhb/gpg1WRjslkl8md3T2MSxoYhB03TuA27+Et22ZppwlcHZ36sUQ05GwuMGkQ1daZklG4GKvVoc73ls6+gP2XwDQtD/qah7wpclhdVHRpWf+5v14t7HDS2NZJbzHCNYX9KpqpzvU6KHeA9scr5ZRKY/zvQohTioF0ZrwMOhIISROXmCmsayVOv/69gH9/FiNXjbGuOtKZbimDiymlOgWG7LNTrettB+H5sn+PlmIDWDigRW4a/qDQFF8iR3IBfd1A1bAJoylqup6eLYJPf73HGZ0Mc/Ejn72/kxgRIhrQix0OBfxA4PBfIZn48heR1sYzMkPUdLGAENJpKVkEe72YIYkjpOMvUwplCrC1bzD2INz7+Tb/JeKOoSjP7/iaAERM42v3Tgf0xesx9uMfbGcyHy6FPWpccjQNnzWXCFX9SUW5obMgYRmRs69VbTk/vLYSkRUrjXWP8s2XqVX3m06d6IqeRtPGg3Csd7Bc2/PWDBNDALItQOgneMcv+DnwMt/A7K9eKDpkkgeF+7m8rFVFzDft/dmsW/Ny3jHWt+OwwhIn6IrNn3P1wN/szb9ppQBrHoYuOdbwDXvCn6PgsTx2Hm/4JbNQtpIRuKYYnDgmimC6/T0szhv54/wXPMfceQ+n0ALb+LfmvIKX1WVer6Xo/7owoQlVMOlpTKf6X06Iln+VfpxvMu0pW5jjaC0pFDwLySjHdf74YPaKubxVqMN/5WOekkdypenInfvwm1o72Orr2XaV4oz0wTu/Sdzk7VQYzfe8i/kIvUsV+JIc2Ofbv4LpjZdw6zDcGwclQ6rjUGPfFbBYboI3MXT2NXZH7mZxLJJvO4DSsmCIS6oev7+EftSPahrZyNvqzuFTUpFnF6WARf8Fi1xVMPlmdsAAEcXd+DjoUvDYTN/ikmDq1jZ7LaFjtuPGfy4om/LbcLM5nM8r5bnp+/G+NAa9DQrCEGJYU5AYHgSlEjPuQ9u/xL1TNy/ASmCRogmHm5vulqeSKai6mL699H28h0AnDV5zbuAh0+PJBtTDEp4k1Cjf7/ZGnwwZNcRDU0jkzgG58eb5vwa6NgQuVy2kCGgfR2sHr6N2Xt7XsJp634SOaPCuHVe0JvmYegKOoCim+f8e2XmnwDs+SViPD3QdAkOMRiMOZ7tKaecHY7qbOt+SnrY3+HFSOZJvlhto9f/vl5/bokcERFYXOXLD5lbMRZDQKmIYn8n5jX/Fj9JP8MtS4b0YJD5f27mPrzc/Btu+lKpiNW7e/DV6+fJC8/3IeOoGR9qsDVDXOQYXlH7cnYf3d50FV5s/j9Jn7F31A8iGg7r0zum4K6mK3xJOrUPTQA15sywZfz5R3rkDoBMkMi36hCOzDnGyM4tscY+AeKiQTjWOQyO2p1FgIMQjD3IIhIKjvSlOXyAzL0QuPxopTY0FbqZz0+8dC4uvffZYHvdJSJQ7wimjyKTMoBtDDfeAA42emFZBEVJ0c3I42CDrRITqS+mqmrY7o8lQWi2fO7j8YZ/SB+/0NdtZ11e6A3RIj7h6PbvQeiFKbVxJJ5aEQumETz4l2/nSx8mItiXLCZFIRSv8ainv4eXOIdeuftlkWGvpwxaEtDB9wxHj8vhls/ppOf1wehDQI0VAj1rBg4zukGI+OLi4b7vBv7cvNvm/PfnS1Ahqu5f3Ip85EIQnFdJ4lKHyGKW/+RZwJXHKDsxAKBErLgSx48u5sw7hnons30SjDXYUoWD82wbTyCq2vdg88UYxZFOHELstXi8uQUHoxffTrH2Q7u8XLHEVM+egAGPcAhLHJnxNiUq3oM0s6dMpzrjIWB60eFuNOo5N3OfNM37O2fhG+Z8HIYDeBN6cGb6CeXyeTglFQojccUk+9/Q3O7o6oFR4MWwRcB5iYu+jp1oCRmTHYReYMonYF77HmnbZI7fwmfPopaz8KW+R6knfv5Jxj4cZbRTbyIWtRE80Ry1G+Wttc+Yq9A7GGQUMM+J1Y8AVx+Lt2Y3CMsDCN5qBAmxiMq2V4+gnyyLe17d1XQFripdCTxxJt511wf5ZSjikG1yj+I0SqUSbnphM/YL7O49LLsbRzk26Hc2XSVJDLzaeiBAlBeJvYd8wrQd7hkcUwkDwDhurGY+vPsDtQ892vRXP4EVtfN2hQOjGHF4jb0rgKFuRGLYhvL/LeIdtlzCkaHFwCu7IXFsoCLgTKyxnauwvOUMfM30je9ZHtSumWmrT8k4j9F6CVZn7I2w9dDPc5MFpS/B/LxNmgZLKpg2Ta4Hwh+nn0V6w5No3S/emDa0/I+0bq++mITjhNDlx2RsGE2EVlVlb0is+oMSR58wSMHCu4wdWN5yBj7TJz5kTBCuVz0DJCBxBIC+AT6hPTFEhLP7LKj6M2YvXx21XDJlTNrQ94brQtGGkHchoYm865qm4EepZ7B8h83BjXivlOBH6WdhWZa6J1UK+SF7TPKKNk+GZWH17iATqJK2h2GJXAAr7rf/LWh4lSMlKfHovn5rxzw6IydxdR3DsObGOEQvfNtaTsFHTJsz//nUcixrOQPjQtLGSefM8C7uDy/dyfQKOcrIe/Zc4b3YYBDsTGIy0FZqrMrsu5UtP+G+m7VKEB4pgYvW9U1TsLjll1ja8nOcnp5ZdnlMlIoRb9n/uvRMHC6Q9MzfHGXc/XzaEiXVVB5k65ulbfOhIT9QPZ3/7qYrML/5197fYcJRdSfhqTj+ITMdu3ZLVJIBYGtQg4UnNX6HuQc/Tc8IPOMTjk7buOEi+F/3AbIeWPkAv70VhFUqYkxzuiIO6HZ1DQaIrj29eTSl/D3sysytzHwpWHiToe/D4JSCEwOX2ofeYVKMOKZXcBt/zHD6P9cr1g4Hwdq9QQGMDldb21Y04oinQTg2UAlw1F3GdtkE4Ummb1DMIg6LjsRR5pY7Ui38Q2PnHn/xTjpnBq6e5atYhReOm+f2+VuxqV1+KWQtvPEtaeHiPbhTLU6YKi7M3KWVfpA0AwD+krk38JylMtNCEY7h0A0uUozn9EHw0ee/jw/klnvPjzVsFaXjh8R2giYIl2FAQnZ1ADA0xL+ghA9g1nwqlYrAhpnA6kexcZfYvqglV55tFSEWxsS0Jfn8lXNw/6vyCwrvMA5rAXwhtQzfmmIzcHQljgCAYi7WAeLOLRLwnsnHj9LPROxpDRCgrS0aSwAAIABJREFUmGNKkMuFa/sKCFQCN8wE+qO22UxYpYDTIRa2dIgvLGNpQi0QjqPyKkMs9a3/Sz/ETHtqag7zeaA897IL4klaeYjYYzP2Kpa6K433gFLJdM+lHr4HbB7ewYib+xFjo/f7rPuWRN57UHAINT6GtCNpkOIQ7p56nVaev8+NqvGZwiiCcogcmNjlM95T03SsYI9tQiHAHJq/SS3cjMiEpNQX2guYen/B/Lz+ORRRNdn3mGz1RdH+u21/H25mha5ywPIuXS1Y/W34YGEl925RDgqloIpuCWbAGeP7QjbeLpT8ZjDwGcu5z3D2MlIqRgj7T5urxYUWsjiitBfvZ3jSBRwHTsUgU+7a2XwTgmh+ltdg/nyIeDVvW42t65K9z1YD6Vo3oAEJaMLR1mkLvKan6DEmI46gM7F1L4aWVUIqbec92AhyZGbOm4fffeYwp36/Pf+RCnIqVaQvHzejcQYPHi2elt051JTl4dr0hfHeoeiFp5nkvEHiHZissQmndQk1E5bHNU0RmVdVC83cQ5pEvKo+8morviIs0QdLFWXfpuV4/5y/AQAOfSffdhAAHm+WeEmUoGcoHyQCdECK+NOjq/D5d78Zb2a9z/ag2xqFiZwLSXMpKH11Xd4v3NKJQy1Tf24Wh2IFHfak2cQCdssDC3/I3IrDSFjtnKD/yvdhrHbtemCprAEAHvsp+scdg7G/FbTf7RpioVgqoUlQjwGCL5mvRZ65oCXntBqkqlp7OWAF/z45PY+ZdoIC8UNf3F5Ytw/HC7bNN4FtbkCjWCwKLwQBG27nctf37IUYJy05iLnNv488e7T5Au93C0PtzEV3f+XHSZeJwAp58dqmPbgkc4dWOSmG9K8ZBSnxJ4JM04jN0PHPhc+mxPbitIMo5X4T7XWDQYksc18MEXk8J1MthrrXaQKCFEr4kBElEEvFIopWDryNp5ZWagff9x/4IYB15o8SL7updwcOtvzxsGAqOf55syHfa1jw1O05mg9LXn4WXzGDXlkvzNyF7YR5igMAXpw2Gf/oeRxoZr83QHBX9/8EnuWLJe5YR/Or4TgGs8zF26f/G3AB30lfPaIhcax3UNyXvQ+cjVYJRz0MV8KQEdi6sfNZmFSwN9HjzSC3Zk7zH9D3jy+xsnn4WmohvpiSc1IOZRjPpxhhImjkkanpZs3DL9ouiDyjL1uupDCM8Gb8VXMRV0KTojjQrnMAHo4zd3HdsRNEvY2t2Sn20krjkBAzAQCWb/alDwdtnC7MT0uj4sAEiajPqsIl1J94neNc4vK34oLJF3Dzjy8FLzcfNjfjlNRcfO+fi5TUs8PYtLsjlsTRdOyl3rRvPpCLjgcLbwpx4QkhGJtXH/dKYGxfqzwRAJCSLdUWwADBP5qujTxzQauX69jP1RuOQKd3abd9W5YvcVgmsHGOwOm7dW3JB7AWqbEWsoNMiWWSSGtKb1jn3NnT9L1GsxiIo5ArSxr+9dRC4XsWM9McUNQACOEPGfGe70L0PflCkNg76JVL8KaCo71CMZBo8CSOIgYEAHw55TOYlrYewDnp+zGKQWx+N/0Sbm2Khl2oJ1yWmZpoeW839uJbL381YIP9YXMz1rZEHU6F8WaJ4x0evPnPkTh+bP0VVCgVHyJ7zc/2PC6s0/UQTEPnBFdl6vCEDcMVDcKxztG8/Hbv9xEb7sK9TpD5lGVvcO8xxSp3KU/iqKeqCgTVLF24F5Rx3baKytWZW5h5aemjLlgxy2gchD6806zs5aESOIhD6IwJOdq4qekGrnTynMwDuKnpBgBAWiJx/FX6cXyQo6LxLxsvx9vm/jzwLGMU0UNGC8t0wbJf+33mQe93pVX/jjY6Yh/mzzb/Cd9LzeV6ygOA6zI3aZV5aWYqtrWcwvYiKEHX7KtjSRyPgkI4gxAyYalGHXh1k6t6OW3sb8eoq44SppQFPKcvhtmB8mKJ1hJ3NV3u/eYFWtfFO6d/Rj2xZQGWhRO7ZsjTJohDjR6mxLLe0Kwh7XJxSeb2yLMxRlZsK1wmWLbq7+aocyaFT/TwvY4WQ4Tj6M1PY1LO/v6PLvkdil27lAlHlsMUHjY8cjFOMtnhdGQYM8KIAgBo1hQ00IhrQvIucxdwwQTkt5cXqksH9zZdGnlWrZjDwxkNwrHO0bwmyMULT+rjTXFMtCsy/8QpqbmJ2TDRXLy3YD8zBEW5+O6+v6HjAN9V/6dkeu3DDCz1DxV1j7RE4ggAP2Rw6Fx8NfVq4FLyKXM1hng6HQ42WUdK66wGPmCyCWJVXJaZiiWL2Z57y0EclcfT0zNjOcdJAk3Z8iS/SaAgsZjod0JK5HfKNRiuzdysXO/HiCRsSwyEbUgrBZpxlkERv0qLOesqYGkRcEFKaNuhQdDkHCJ9TfntHA5oicGoZWlhjEZliZLY6v4VwmF94iDwKxfPiUikeAxZmcSRxhnpp/B+c1vkeRuZqFxGAzZGlTln1z6pZxtcDlh73qExVW3LQgLhjaqJBuE4zKDDRQNsEfmlman4qLmRm2aVNUm5PDv8gI1XWn6l1RZVHJXbgkM7+Co2LFvO4YwxjI32lib55qmrUsUCzXi4LDMVhxtd2Gy9hZv+9tKXy64zCRzCcHygi1sHzpYnArCLHFJ2XTIQkZv+CuKgfjHjqRpoNgrAC5ehePNnkGvfgrbeICOlrce+3Da9OkVa1ns5DhtGMli2k5VGoVhArqBBJN/8Sfvfh06rTIPqDJ8y+fE8dfClVDRgepL4XYbtnKlW+HjPs8L3hADIqu39R5vlq+C7jvAaUEc50koASPfz45NWA1dxvMWqgheqSYhifM/JtUCDcBxmOEjBzfGm0cdHnoncIy+x3qVc/4IWtcv2cMAjpU9L07SNOvb/t3ff4VEV6wPHv7Obsuk9JIRAEkgooXcIvTcBBRGQooJIVQQUUARs6BWv96fYOxa8YuFarw0vYkcEGxZEpImItFBTdnd+f5zNluxmk0AgIXk/z8PD5uw5Z2d3zpwz78ycOT6X5+nACktH8edBVrbjWCo7CaXqaj53vc7nYuhKu52PnfXP8MWWf+bDNYsH1kdPZ6qdj+4k4K9vefi+2+iwbC0nC1yVjwJb1WuN/d1eq7KTUKn+OHSc/Lxy9FYd2QXW8g/fPF+lqDObNbpIe1PZZ3isCdp8eQ1sK3kUTUWzSxX5nGtq2oFVm9hpL3nSm6rsdCYH0hI4irPp8oB3S11ndcqN5dpn3diy3ddW3fxkr+e1zGbybGE0Tf/M4++igHFMwaIKScNJU1iF7KcibTE1KvE993tiHjL5nzm1JOXp4a4KfN0HdHF+CbOEniMVNWS4qIJ7Y+EVzCg4vREEhdr1CJL3zd2wFOtBd3+/NLnaKA9NFr/LOz/sY9fBc9cT+7GtqcffywtHlXj/ZXSdrHORJKYUXHtOPqdIWfNq3GOfM2+V/4lXvBSc/RlRy+L6wpIn36koZ/pIkJ/tqRWUkrPj9sKx5d6mV/7dp/15b9g6nva2Z8LfoxXE2XOYcPZTc4YJW/Mr/xFC5SGBY1W2s5wXZoeDAeVrqYkL85x7uCJ709xNLJh/VvYLcFH+0jKtN9Ktwn9hp2yes/YG4EdHEGmyeE4unxDhCiR/GLTG+YiRE1j4qQIu7vawpDPeR0XZ77ifIzEmgrZ5Dzl/E3dFvW9rbDm0TD69BocA7FxTMB2A921tTjO1JbjiXTYMec9j0bu2tme0y+Iz3B7VIRwk8oz2eab2V/C9Nx/Zm/OWvSOndBnnIXcTFOQqI/nK4jUR1/TCa3jCWrYHvSwOfJbPg2eyOXgK76y6l4fuWVTis9cq2md2z8Axp01LDsS18rlubu9/MqtgZpnPO8UVaDPD8m8pdb3Jw/u5/kjtyCuxk0/r88pqla0XNxZewc60i/2u90nwNSU/YqUkd6X7fbsoaC1PQ8PpWG3rWe5t/hF2HdPL0bAy0ry+3J/h/KzC0azv/sJpb382tct7kHmFV/G4bVCp6260ZzHbcZ4H2K5LvgWiNH/ohNPe9kwEB57dYxFgcsHcs/4ZR8s46V1xH7R9pEI+/7rCKWVab6PdaJA7oiM8bosqzZuV1LBQUQrzJHAUFSU0rtybPNbwcRYMasS2jneUeRuTxTW0LG/EswwtuI07Csc4l/1mTy53Ooq7tmAaLXuMKNc2B3TZK+ctOvYudZ3RBYv4RruGnmYPnEKvpsYsjevtzbi18FLUFe+4NkgxgprtzeewK2MMgXXbOQPH2f0ac0t42Z5FaPMza2RwiPcJ/XRP8v78x9a51HVCwozfOzM5lgNEcbvVs1X5y6AOdBl9PQ9Yh7IuYx7qqPfDvwtMIbxdfzGf2ZrwXZBryPT+FjM42nAkYMzwG6GMYW6BUUksLxx12t/LS92OtG/bAa78kG1Jg7m9cCzXF7to6Zg0j7+39F7pd5fuPY6v2rpwh3XsGbVE2yNKrkBt6nQ/x4c9Xeo+CghgZP5ifrHX4Ut7Iz63NfF4Pze4fGU2z3EvTyH+K0p/aOOc9JO9Lq/YukL7KZjdJt7KV64g8mrL7Txv7c0n9qblGuybrA4Ro47zf0EPVvg08yV52DoEW7E8jYmJJfGy551/b6k11PlaRyTRefhVXDPef4AFcCAkzWvZ0MLbee2Oa7gvezXb7SU3HjVLcTsHXvw0SQPL1/i2w16LzXbfw+2LPB/muu/wn9aLies+lXqXPe5zXR1wdkan5AYmsLK2cT7dotNOez/PWPv6fX9o/q0AjC9YwF6L/9/F3ZH6w/jI3sL59+9ZpT+WoCxmFszi6oIZHsves7ehe3ZaufbzfsgA8twmNltva1biut/YM0rdn6+K/iks/E00L9u6o/1UHb+x1wfgiA7jaNZFpX7WPh1T6jof2rxvvynuz6hWFEaU3Ji72+4/+PxLR7PJray8EH4ZJ2t7BiS3hFxfajr262gO67IN159dMJ3P7Nl+17mx8MyOtY9szVlu9b7Gziu8qsRtPrM14abCy+jYdyRM+cjr/dwSGtTcPWS9wPn6JVsP5hdeaTSWDXsAJn3Ap01dDWeFOfOwDfqns36VafqD+qY/S/2MIqGN+ngtW2V1NRCtC+4JSa4yUZQ/W3w0jpfEo8e8v/dsrO4+szXhZNvpftf50u4a2VWgyt9gW5kkcKzCTkSklekkvzmiu/P1lWMuJjHCQoMB3gftwUDfFRSrJdb52tJsKC8vmUT9YTfwtq094wsWMLDANfX7y6rfaQWS6+3NubZfQ2z1XQV8k68KTctLnS+ftvp+VmRBgPdJuUtWLe4u9F+Ji2/am+xUt/uxzIEEW4yhcXZMPGEbDPGZrOz+Ma+1fAzGrwEg46Il1J3wMA0Sw7E5KtYdM5N54fpLIMjVQ/mXKZFnrH35wOLWSwA8Z/M+qRUJSM/xWna3fYyPNUv2dtiFpa5zW/BcnrWWnA4G/IOIk8ajXeolJdClQTxWx8lspz2RtLxVXKvmEx0eynLraOrUrk3MIO/A+ee+Kxk0fi5/Dn+JzBxXQ8GOpjM53sm46AarQuIwZjNrllWfifNXONern/es3+/xZ/9HaZ9XhkdlpLShwdRVPGYbQq7bPXd3FI5BXfOtx6pZLXM4Ws841vIzh3jtqqjH8U1bR+YUTucFW29mdDLucSuIb8LWRjO8tvHlQetQDjUeR+F0z0fVuDcstO4/Hmt6T/K6eAcI+Y6RAD/bU/mrx3L+dd00Hmu2igfSVnCrdZzHulH1Wnj8bUfxeNOSf9vpfZsyp28WmP1fwL5OMCqC4QmpfNTkVhi0nCSrazKDPLfAsXf/4axtsJD3rh9ITgMflTZLlOt1m8v8fu6Z+NqeWeo6D1qHMquP5xDtiLhkiHQF+bu73uV8HRwUxJj2denexGh4yg+O4zHrIHrnF3umWPZFxM//lp8zXRXxqwqu5Wd7XQCmXdSXsF4l9zhY3OP4yGRCggOdwXtxh4K8GyT01Zt5rKH/yR4unXqjM9+PEs6hk77vRTyuLajJrvvLiqdjnc11zBVqM7S5nI3a8ze9KH+pzwq87jCdCd0aG2ko1nC2OX0KJ3MW8L1y3Yu/w9LYZxoXWy/jYesQBuffTlreKqYWzPZ4/2dt/O4f25tjv9zzto8VzV7h6w6+JyVbckET7hrrOlenjbmHz8dvZ3C+/wokwLH0AQzNv5U/dazXexPHXc7rds9rwIxh3WmY5LqupOU9X3wzHnarlANE1WuJZcE2598TChc6Xxcf1TEn0vMxRssLR/FH5jg65N2PVRtVwpdsPbw+M2j4vfRomMD9Y1tRO8piXL8bX+C13mvaqI/ktMzmwUtbe72/1Z7CXrffInThr/xQ7PaFpYUTPBo8vtZZLCq83Gtf7pKvXUdgsjFq4H7rMB6wDnWeMwEmF3qWs5VujQzLCsfQIf9BZhS45nD4LmMSaRd69qp/F9mTJ63GBHG3Fo7zyoenrf1on/8gNxdO8Epf8ZEvuW1msmD+Ek6WMqfAS7buHn//kGIEgUsKJzIpfpXfbQGaL1jrvAXA3S1LlnFQR/jYAsYWLuJZWz/CgwOgdku+SRrp8X7UrHWlfm5aH1dg2j0rga0pF9K//2BoNQ5S25Ez8hqs5hAAAjN7Ym4/mRZRp3evXz7FRsk1HMwN1is5qo39rw3sDlM/cb5dlG8nHL998UZDgN+yJmPr5cp/jx7zTjOghWfD+syCWc7XB0e+Qmg9z3KXlueZV3MLpwLwiHUw+aFVZ+RZWUjgWIXtO1bA8ILbeMQ62LnsN3uyszAUSR80B1qMgUTPXged4TkkR5uD2WpP4WHrEMYUuO6DjGk9HEJiId4YJhBpCaRbw0SmF87mM1p49ELE16rNxQVLeLu+ETTcUDjJ+d7xsNQSh7oVPebBnGMUrieaP8/4goW83uO/0Odm14qtxjtfNkuNpXf+cj6v5VlAgwYZgex39nSOJhiFMz482BmgzXe7h+UVW1fn67tGNue1GTnkxWdzopERZJqyjCDvF3sd1s3rAcDEns0ZNnyUZ8UWMJsUVkeRCQt1nOxnfwdd5gCw+6I3WGy9nNfrGpX+E8qoAH0SUfKwHtXQMYRvxBPY521nVMFiWvQsvRfD3aCr7wfgf7YWxkUtMoUXrcZ3WVA4mUNXbuSd2V3pNsh3QLqe1tBxqitNsWk8N7kDt102mBM6mJXaSOPe3Dzap8fy7KT2XNsni4aNm8NNB2GEq1fIGmFUpEe0qUNI15nO5dGREcTWMt7b23wmnQcb+RzXcSyJkRaYtw3bnK1MzGlAt/x/sa6e72Fhp+r1ZMrgzvTOX87x0WtK/W1a1IlieMvabI3rBbguMO496oFBFiLHrYRZmwge9QTM2kT3/Hu43zoMcAWOdreLy8g+RmUvqM8iskbd6vOzP7B5tspOmTqH2EseIDDYFch+mzKGFTYj8Lf3de3H0n0OzPiKw/3udS4rOpbrz/2A0b07kBobyt0Xt2DFmFaeldKmIyDAszJiGvsik0cO5QNbK58V2Ct6ZnN170yC+i/1eu9Vt0mkhvYxfsfULmO5b4zx/Ranuy6IeQSzKvgSjmsLdWLDePKydqTGhtKon6PVfORTcMlzxutLnoN522DuL3DBvXDdb3DpKzD9S5iyznjd2NXLB0Dtklu6SxqS/Ke5NhTrZXbXJ/8ujhJOeIjn/c0pWa7PsgdHERRg4knrAPboeMItbo8QufZHgq/ZSO9rHuM37Xbv6axNMNx4PEijS5fD3K3sGPgc79pd6Qw0m6jV2DNw2B3Z2tlariJrMzj/djrkGWU8JNDMgPx/cJePXvr8zvNg1DMw7lXnsvT4MB68tA3HE/0MCbdEwtxfeLvf/wDolukd2H3WbgXHpn0DSU3hqo9h0gekLNwM415lQ4bRcBIbl4A12ficT2gFF/wf/0hYxhUF8wAYnL+MTToLi49nHEZn9yEwxQg8H7UNgfAk6DANlubSauJyQvsuJPFax/DPup050dXVaHXQZASw/05dAihSLl7OFm0Mi33H3s45M+ZeHUshZj5b0Ivf7xhEnVrxcNMBo0I54ytmjehDmzadvH+f1hOwBJoZ3NzVYKqUolP9ON6a289r9W149npFXPos4RntCfJR28qqYzRAXVswjaM6lF1Tt3FRRyNAdgV83pXal92uawB/xHc28tHND7F9OZXSiR+73MuIgBVY5xpl7UPHda7IB3FjYfDd/EUsbw/4hPWD1hIUUCyxS3MxtxzN05e3Z0jz2rwxqwumK9caZbjXIjAF8KK1B+ttzUhqaTTChXSchMVtqOekLunsnfglFxbcgs1tOHKkJRCV43m+f9o2gCmBd2BPMBoI6sRF8Jytr/MceFHwI/TM/6f3D9reuPa37jee5dbR3GJ11Sd26CRetxn5e7RubwbOf8753mEiuLhNHY4QxhEdxgrrcBYPySY4ti7M+ApqNYWmI4kPD3bermFHOfdXZG/6CNqnxfK1Nhqr3BsuPALfy95Cd5lLUpSFcR3r0jnvvhIb5Nde73aMDb6HRgOMgGzJ7Fk8MdNVN9zQ52WY9jlkDYDUDs7lMWFBBNd1BfB2TJyMSCc0KICHm61mcuzTxhuO+s7jjtsK1kx3jVKyuT0uyjkxWKTrXGcL9057kzRXoLXyivasmZ7D9B6enQUBtZsbL5RxPFiaGQHdwPw7nHWYo4RDcBS5FuPzHrJeQJ98oxGv6F7g5rXdOhNajYMxqxjTvi6fOm4/+NXsajxca2vFdzqDkzqYHyxGGfvT7D1nwOGoRpi7eTY2/EkcFI0YGnY/XPkhUy13MSD/Tt60u3qnL2hRG5Sr3J7KGEC7tBg6593HXzracR1J5M76K1luvYQCa9WbAM4f/w/QEpVqX67R+nKXdTQf2Vuwwd4IjUKj6NkoiSd2GIFSdOMe0LiH1/Zq3KuA5qFVq5m2bSpBQUH0O+IawpqWt4o10zvTqm4MXLfNY9tgx4XDpGBGryxwzBFTv91ApjbJpFenNLSeTs7PufCqETjsGPspQ1Z8wg6LEejdEbmIhUdvAyCztqOnL6MHLM1lVF4huRG/M7BrAzB3hi6OE2zBCWcaspPD+W1HCgW9L4Ssh2CpI5BrPZ601Ubld8e0gaDttDAHMLlfG9LeW0V8eDDH4r4l4q8NjLh8HiQ8AXm5hAYZh7tlpmvCm4iG3RkQvpqr+zcnLb70iWqiwkLg5FGCAxxFJzQWei+GnjfSxmRm4cBARrVNhbBc7n37J1au/5nVk3pC6hVkL3iZLZZJvGXuzWDbWqOVP6MHLPobAoIwAauXzYVTh6HYLTJpeaswYceOYofF1Su7YcgHtA8Oh8WHWPHQZ2zafZQr5gxm/oK3mG+dwnX9GxKbYpys4zuP5KpfU3nkd9eFqKD7Ijp0NQJfFh+GnZ9AmnFxrl23Ptn5TxFgUoxpn0q/JsYFo6t7xdIcADFGJe1Hez0K3FvOTCb09b9zYN8esmo5WjaX5tK+6P3ObtOqhydgBhZfUIuFgxoR+PUTsBPeDOzPHccG0du8iWds/VkbGMrkrolM7DyZQHPp7V6vzTSCnhef6UPWwQ+drdiP2C5gYaDjPiJzMAQEQZwxxIq4+qxZlMqBrVnw2musjR7JBbnP8z9bS16e2okGieEQGgRLXel/xj6Q7bYE0gfPZcnrxrPI3s54GfZuBuDk/H2EhoQ4fha3C0qv2/i/x76g9bjb6dbIrUUz0AIJWcQkZPHFLz/RcefDLLeOYuzNqwk0e7auRoUEsvnOMWAfBcpkXLC+Ww0//seo5BeegrpGRSJ0wmp2myD5WeNCmpW3ksa1Y3jN5Lhwd7gCOngOjep2PJ+Cp3sRdOAHowfupoNgclX8rh3Zl2+eH0PLvS+Qr4L5X8JEbtg2jJX5riGsqnYrj9+Lmw5Ase9BWDxkFusVb9AbbAXG99La+FytSbvR1VtUdL65qnAOFGruGdGUi95y9X4dajASLp3Ai4uGcUnAOjLynmO7xeih3TNrN9uWG8/zVCa3y+HSXFd1feZGTCGxWPZpbrFO4BbrBH4Lcls3yqh0ZITCb8sGQdEIrKLjqUhELeq1HwJr3iYl2q3xr1Y23HSAn3Yf5NPth2jfoBY3PPg5ef2Wc0VoLFt0Op0yjODIZtccI5T/JU7g+km3w6nDvLwrjLiwIHo2Kvne9vBJr8HhHZyKacylS1fwvc7gqSs606V+tJEPgSEM6hzLbx01ZpN3oJLUqCPJSY7KYXJzj/wJ+W0XbIc6UYHkj32PoUsfITq1MT2Bhy7rwo97m/NW3mS2rNoEwJzCaTwbdKfnByQ3R+HWKj/PezbRWpEW49hTJrJNJnB0fsYt2gZ7NnBJagdGaaN8ffjzftZs/oPtywZjMu1n0X++57kvjBEVtd1/e3OgxxA2EhvBnJ/gHkeP5tJSHv0Q4NnYsMuewLper9KgY5LReKMUmANZdWVHWB4AJ4B5v8LBbXDibyLCjWvOGntXLpl4HR2TXL24cZNf4tcgE7O3/A2feHwMHyybAjcbIzjS8laxJqs5xb2ZeRsLBjbiGuCa/sWGrva6CT40GqrenWM0UmxfNsh5btraHj69KZscs+/nKsaFBxMX7vju3a6Dbtcxf8FbAPx+UX8Y4f273TTEaNh+fkYS1sc8z93Z/Sfx+vFAhn7v6rV54apOmBI+B7uNj8wBvP/jX7St+zKEBPCsFVZ+vgPWFfuQBr1h0d9kFyh4+z2et/Xh9tv/hdaa722arEVBXF04ix1XDCYSo3Gvj3kzF3ZuRqdBzXnp6z20zH+M+QMaERLkOMclZMG0TwHo+/UejjoOTY3iR53GwJg3WXl5OxIjLdzgSMZ1L4WS9vUqWqZGw99GL/Zx3I67tC5wxJh987bhzdjQIoXZjx7ljWDHpHuX/xfeuwn63kyq++SF7SYZlfaluc5en5dt3RhpXk/7zr3BZIKxjmd/L41ic2gOrYC7rhoBD/8L/voB05VrCU0xAskbRzqCw1M9HNd01uDKAAATxklEQVTBYNY/tRF+PWDUCyn6rkbg+ELcLLKHOSbsmu145IzditkUCLd4DjlOTCxDL1rROdfuuC++5yKu3dWFn7bZuNk6gUsC1vF6QH/GzX+cKABtZ8erW9j29R7S8laxqs4aOLCblGC3GZ6HGo1syy5syqYWz9LpsfcZ2t44F2fkPUeDhHBGd0gjJOc6hm/9HF5YRVBoJIeu2k+H295hoOlL7gt6gCOh3qP97s1+lTtHOMqSyQwpbdhkO8x+7ePZlWndIDAUJrxGSEpbBn62k1t2HGbrJZ/w/id/wPaDHA7JwMpu8iVwFBUlu3YkD4xqQoEKJMDUluebJ/PxrweoHW2hfkI43FzKDkzGqWVshzTYBhGhIV6r5BU6DliT2WN5cKCxrV3D7D5ZZH/4BLHqKC9kdGNKTNGJLJTBzUPhVbBHppBd27PFMy/A1WP30nTPFtIIS6AxNK44t56SOg2a8cOA/sZwCb/f0Uhrv+wk7n5vKyPb1CFiv1uPS0Qt458PAWYT78zzPSTWl6D63eD7lzx7dJQCcwAKuKq7q7J4Xf+GdMtMoEWqMZHJPeO70PTZx8mICWDwqbXGiRqMoMXjQ7yH4n6xsDeBZkW4JYCTeTsJWp5OgLKTmuiYJMVk5sWpOc6WwQ039mbnwZO0red5Mn9gXDtw6yALqt0UAhx5bzJBejfne2HBAczq1YCumQm0T/fupXKyGT0IJwk2nrPlRoXGkpDhZ1sf3APCjMRI/jiWwP5GE2HLPiIcPT1lCRrdteo/gWb/SuQYxrHbLi2GDXsbGtPdFw9ggNiwIGJbdeVo+nbsO/Jo9u+eHCOU62NCiA717lU/0et2nn7nZ15xuyetYWIoOEZxuk8g465j/Xi+XdKfqJCSJ6RqP2EZTW9sy3FCfabVyb0MNx8FWf29es07Z3oGFy9N60Jmsv+JduLDg6HndcYz+GLTjcYCNzFhQQTHmmEvJMXFktMgnk+2HSA82M/9kv6+hzulvCrnAA+Mbc33f+QyvWd9br31UrqZvufj63uy4sNfGdQyFYIe4YgOYek3USweblSOFlonc4t1vEfPcZ24SEa2qcOB4/mg9pXwAxhBdod018HtK7jyt9z1dRSPT2hLdkqx+7fNgSRHW5jcy6hcfzi3B+mOhqyfbx1AgGO/DZMiuKBFbWb1agDhERCeyMiyzBsSHAFJzbBozSadRXp8GF2yvDf0lf5meY/zfHDJQWmzzHT4HGLVcQgOYOEVo2iWYhx38eHBdHN8TpPaPbj3g6385xv4dNxvbNq6k/vW78GMjZ/d9lcnxvs65Uqgj2uByQR1O6JwNfLffXELbhve1BkI3Ty0qTNwLFWkn0lcoupCrtt+nI0NCtDEBFoZ3SkDfF2ztONaq0xQzzgmi85iGfFhdMzwHPrbup7x9+zkGGfg2DfsZfYezGWLUjDxTdj+P77L6Uekxbs8xYT6KWPd5jkDR+dXKZb3oZP+w8t/7GdQi1TKc2erUv7LQMvUaP4b1JR0q+fzmOvUywRHLLLhht7GSBRw5nnfJq5reJgZo/dqnY8PCAgirNgzjpVSBAUoHhnfhv3HXJX8k5EZcGIzIckNUUpxSdtU6ieGcWVX37cIjWhTh1nfTCH891O8aOtBRnwY/72mq9d6yy9uwdW9M41zp2M08yl8XwOM9Lk99im5pXF8XLm2xPXdNZv2LL9aT5Jp8rwmnpy3i2bBIc7v7+y5Nvk4L4e46glPXtYOq93zQr4v2KjXxNRtTPO6cZ778bU/ICS0DDPGp3aAnZ9CqKNjIdDC8sv7scymabz4HZrmPU5cTCzjnN/NxLIRzVk6rCk2rQn95RS8+hJEJHN1wQzCVB53OI4/pRRt6idz16SBzoa3H28dhNmknPWH2ESjrCc0ykGFB7Np6SB2HuxB+0fb82Zbz5EgXy/qQ2RIoLNeXaRV3Wje3fIXb87qAu5P1QpPgBtd92lO6FSPHg0TiA200qpuNJ9vP8jFbetwZbcMUmP9nPOqIAkcq7Do0CByMmKIjnZV7Lr5uNiXJqpec4hJQ/Vfxl0H0kiKstCmXgz//mo3HUoICIIdwURSpAWTSXGCEE7oEGevnYeZGzGFxYNSbLqpLzhu88lMioRDxmuvoS8lMZlh+hdw8iDUyyG8hIvQVd0y2FfsQeFZtSJ4Y2YXmtSOhN0LYd/3xkm4Ig29H3JmGz2NpQg0m+iS6bqnslFSBMcJRRfNOllS5dnRCn6i+eWEvWf0BiZFuQWq4dEMKriNqQFv0DHKNUQk0GyiaGRQYoSFxAjveycCzCass77l8me/YVrrUDo39B5q5W5uvzI849MR+O43J9KleIX4DDWpHcXvkweRb7Xz29/Hvb9Tz0XYfv+Yr3bmktyyLyXd6p5VK4LPlg53Voyfurw9fx14HYJyPYaUFBcZHYfWf3CMUDITw0mO8n2Cv7JrOp3rx9EiNZrejRJpUCscc75x8X3IegHTige6LcY6L7j+gkYAk9nMhB7N2PpXOZ+5WCxo9KVFaqzXhdCn7OGQXXLvS6gyKmMXtK1PQKsMOtWPM1rbz5LBzZOdQwefsA3mCdtgdsSGctdIR09ji9FEA//nVvxT48LZedDE05e3A7dJK+++2LHN1z8BxmM5vKuCpQeFToPuho1Plfh2nyalPwMyI8HVeOQ+3C/QbGLFmNInpiiJUooXruxo9JqXUURUrNFQWZKiSt9J49EuXX0MdQVj2Oz/jW7FP0e1xGxS5DSI55/r91HoVg1ZN68HMT4aZsrLbFKEuQVvRXl3UaszfIzN1PVw8pDr77AEiK4HfW+BD5YQ0edm30EjwJB74N1FYPEsF69NaU1aUrzvbYp5eUYPDpxwBD7pXSG9q8/5nW8a0oQJnco+8YcvrdISaZVW9hnav1jY29+p1EPw8HvhZc9ZjDObtoU3jdfOoPE0BZhNxIYFMb2HZ69//2zPXrBaw29nwosdeTDbGLL4j5HePbfFRcTW4rpfp5KREMaTE9uVuF5RT2G7vAcJVoUMapYMvwIm7/N9izrR5DSIhd34vR750rB2NPh4dEVoeLHzv3ZM8qb8T4AWYDY525KLbK83iqHbIplUr0e50vaodTCRDbtT4oO7ei0yri21XLdZuX/+cUJpGuPZbGE2KVdvcPORRmNmSmsWLRziMaS2iPv5yFJ8ltyYNLjqY1SCcS92hCWQpilRbLh5uNd+nD3sxfzrkpbsOnSSRkn+6z4BZhMZCeEcOXKEOX2zGNQsmaYppV+jqyStdbX9BwwAfgG2AQv8rdumTRtdFR0+fLjkNx/tqfUddc/aZ/9n8x69+9AJrbXWH2/9W497/Atts9lL33BJpNZLIrU1d5/zdYVYEqn1G7MrZl+VYOeBE7re/Dd17zv/a3yXj5b7Xf/w4cNaP5Sj9d0Nvd6rN/9NXW/+m2XLj3Phm39rnXes4va37wfjN9q2tuL2eZpO5lv11Gc36j8Onyzfhr+8o/WSSN1rwSPl2sxvma8o7y6quHKptdY/vmHs78C2ittnGRWVhdLsyz2l39+yz/hjSaTWD3fzXGH/z1ovidSXLby1xH38tv+Y/nTb32eS3BJVaL7v3qj13m/PbB/PXFi2YyTvqLHeZ/eX+yN+3Jurv/r94GkkTmu9LFXrx/qUefVTBVZtLev5ckmk1qtGn166yqlM+b7rS603PF76euW53j45UOtbE8u27pl4qIvWd6Z5L39qsNY3x3kuK2994Yc1Wq+9Tes/Np9ZGsthyWs/6Hrz39TPfPZ7mdb3OMa3rdX64HattY98P/638d03PuW9k51flFpfKNWXjxr7P3mo3JvmF9r06q92+a9v/PKO1g93NT5j5xdnkFDDroMn9IJXvtUHj+ef8b7OyPevaP3Lu2Va9Zdb2+rDS2r7XeecXN8rALBRlxAvKV18bFk1oZQyA1uBvsAe4CtgjNb6R1/rt23bVm/cuPEcprBsjhw54tHjeN7Z9QUc2AqtvWcZq2nsds2CV7/jss7pRq9oKfzl/ce//s2fuXnG/ZSiyvp29xE27zrMZTn+n2Hn7rwv8+fYK1/voVakxaN3v1R/fmv0FIV4/s7rt/7N4ZMFDGt5hj1Tp0HyvWaq0Hw/vANQEHNmvY2VZudnxv2fVbi+kHuykDvf+ZnFQ5q4er5Og5T3mul8yXel1Ndaa58zzlXnwLETsFRr3d/x90IArbXPBxxK4CiqGsn7mknyvWaSfK+ZJN9rJsn3mul8yXd/gWN1vscxBWPEeJE9QAf3FZRSU4ApAKmpqRxxzHJVlRw7Vs77mkS1IXlfM0m+10yS7zWT5HvNJPleM1WHfK/OgaOvO4w9ule11o8Cj4LR41hVWwGqarrE2Sd5XzNJvtdMku81k+R7zST5XjOd7/levjntzy97wONJvHVwTo4vhBBCCCGEEKKsqnPg+BWQqZRKV0oFAaOB1ys5TUIIIYQQQghx3qm2Q1W11lal1EzgXcAMPKm13lLJyRJCCCGEEEKI8061DRwBtNZvA29XdjqEEEIIIYQQ4nxWnYeqCiGEEEIIIYSoABI4CiGEEEIIIYTwSwJHIYQQQgghhBB+SeAohBBCCCGEEMIvCRyFEEIIIYQQQvglgaMQQgghhBBCCL8kcBRCCCGEEEII4ZcEjkIIIYQQQggh/JLAUQghhBBCCCGEXxI4CiGEEEIIIYTwSwJHIYQQQgghhBB+SeAohBBCCCGEEMIvpbWu7DRUCUqpv4GdlZ0OH+KBA5WdCFEpJO9rJsn3mknyvWaSfK+ZJN9rpvMl3+tprRN8vSGBYxWnlNqotW5b2ekQ557kfc0k+V4zSb7XTJLvNZPke81UHfJdhqoKIYQQQgghhPBLAkchhBBCCCGEEH5J4Fj1PVrZCRCVRvK+ZpJ8r5kk32smyfeaSfK9Zjrv813ucRRCCCGEEEII4Zf0OAohhBBCCCGE8EsCxypMKTVAKfWLUmqbUmpBZadHVCyl1A6l1PdKqW+UUhsdy2KVUu8rpX51/B/jtv5Cx7Hwi1Kqf+WlXJSHUupJpdR+pdQPbsvKnc9KqTaO42WbUuo+pZQ6199FlF0J+b5UKfWHo8x/o5Qa5Pae5Hs1oJRKVUr9Tyn1k1Jqi1LqGsdyKfPVmJ98lzJfjSmlLEqpDUqpbx35frNjebUt7xI4VlFKKTPwADAQaAKMUUo1qdxUibOgp9a6pdv0zAuAtVrrTGCt428ceT8ayAYGAA86jhFR9T2NkWfuTiefHwKmAJmOf8X3KaqWp/GdR/9ylPmWWuu3QfK9mrECc7XWjYGOwAxH/kqZr95KyneQMl+d5QO9tNYtgJbAAKVUR6pxeZfAsepqD2zTWm/XWhcA/waGVXKaxNk3DFjpeL0SGO62/N9a63yt9e/ANoxjRFRxWuv1wKFii8uVz0qpZCBSa/25Nm5Mf8ZtG1EFlZDvJZF8rya01n9qrTc5Xh8DfgJSkDJfrfnJ95JIvlcD2nDc8Weg45+mGpd3CRyrrhRgt9vfe/B/EhLnHw28p5T6Wik1xbGsltb6TzAuRECiY7kcD9VLefM5xfG6+HJx/pmplPrOMZS1aPiS5Hs1pJRKA1oBXyJlvsYolu8gZb5aU0qZlVLfAPuB97XW1bq8S+BYdfka2yxT4FYvOVrr1hjDkWcopbr5WVeOh5qhpHyW/K8eHgLqYwxp+hP4p2O55Hs1o5QKB14BZmutj/pb1ccyyfvzlI98lzJfzWmtbVrrlkAdjN7Dpn5WP+/zXQLHqmsPkOr2dx1gbyWlRZwFWuu9jv/3A2swhp7+5RiygOP//Y7V5XioXsqbz3scr4svF+cRrfVfjkqGHXgM13BzyfdqRCkViBE8PK+1ftWxWMp8Necr36XM1xxa6yPAOox7E6tteZfAser6CshUSqUrpYIwbqZ9vZLTJCqIUipMKRVR9BroB/yAkccTHatNBF5zvH4dGK2UClZKpWPcOL3h3KZaVKBy5bNjqMsxpVRHx0xrE9y2EeeJooqEw4UYZR4k36sNRz49Afyktb7H7S0p89VYSfkuZb56U0olKKWiHa9DgD7Az1Tj8h5Q2QkQvmmtrUqpmcC7gBl4Umu9pZKTJSpOLWCNY7blAGCV1vodpdRXwGql1CRgF3AxgNZ6i1JqNfAjxuxtM7TWtspJuigPpdQLQA8gXim1B1gC3En583kaxkydIcB/Hf9EFVVCvvdQSrXEGIK0A7gKJN+rmRxgPPC9474ngBuQMl/dlZTvY6TMV2vJwErHzKgmYLXW+k2l1OdU0/KujMl7hBBCCCGEEEII32SoqhBCCCGEEEIIvyRwFEIIIYQQQgjhlwSOQgghhBBCCCH8ksBRCCGEEEIIIYRfEjgKIYQQQgghhPBLAkchhBCikiiljjv+T1NKnVJKbVZK/aSU2qCUmlja9kIIIcS5Is9xFEIIIaqG37TWrQCUUhnAq0opk9b6qUpOlxBCCCE9jkIIIURVo7XeDswBrq7stAghhBAggaMQQghRVW0CGlV2IoQQQgiQwFEIIYSoqlRlJ0AIIYQoIoGjEEIIUTW1An6q7EQIIYQQIIGjEEIIUeUopdKAu4EVlZsSIYQQwiCzqgohhBBVQ32l1GbAAhwDVsiMqkIIIaoKpbWu7DQIIYQQQgghhKjCZKiqEEIIIYQQQgi/JHAUQgghhBBCCOGXBI5CCCGEEEIIIfySwFEIIYQQQgghhF8SOAohhBBCCCGE8EsCRyGEEEIIIYQQfkngKIQQQgghhBDCLwkchRBCCCGEEEL49f+ACktYu7nbEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_compare_plot.plot(title = 'Naive Bayes test VS predict', figsize=(15,8))\n",
    "plt.xlabel('ID')\n",
    "plt.ylabel('Car Price')\n",
    "plt.grid(True)\n",
    "plt.grid(alpha = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4900</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7400</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8900</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9900</td>\n",
       "      <td>12390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5800</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>9950</td>\n",
       "      <td>9995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>12990</td>\n",
       "      <td>11050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>8990</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>12990</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>9990</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      test predict\n",
       "0     4900    7995\n",
       "1     7400    6900\n",
       "2     8900    7995\n",
       "3     9900   12390\n",
       "4     5800    7995\n",
       "..     ...     ...\n",
       "496   9950    9995\n",
       "497  12990   11050\n",
       "498   8990    7995\n",
       "499  12990    7995\n",
       "500   9990    7995\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Small Scaler version of compare\n",
    "\n",
    "nb_compare_plot = pd.DataFrame({},columns = ['test','predict'])\n",
    "nb_compare_plot\n",
    "\n",
    "for i in range(len(y_test)):\n",
    "    if(y_test.values[i] > 150000):\n",
    "        continue;\n",
    "    elif (i > 500):\n",
    "        break;\n",
    "    \n",
    "    nb_compare_plot.loc[i] = [y_test.values[i],nb_y_pred[i]]\n",
    "    \n",
    "nb_compare_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_compare_plot.plot(title = 'Naive Bayes test VS predict', figsize=(15,8))\n",
    "plt.xlabel('ID')\n",
    "plt.ylabel('Car Price')\n",
    "plt.grid(True)\n",
    "plt.grid(alpha = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-3095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3015</th>\n",
       "      <td>4657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3016</th>\n",
       "      <td>1405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3017</th>\n",
       "      <td>-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3018</th>\n",
       "      <td>-1450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3019</th>\n",
       "      <td>-2996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3020 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       diff\n",
       "0     -3095\n",
       "1       500\n",
       "2       905\n",
       "3     -2490\n",
       "4     -2195\n",
       "...     ...\n",
       "3015   4657\n",
       "3016   1405\n",
       "3017    -49\n",
       "3018  -1450\n",
       "3019  -2996\n",
       "\n",
       "[3020 rows x 1 columns]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Some analysie with the difference \n",
    "nb_difference_plot = pd.DataFrame({},columns = ['diff'])\n",
    "\n",
    "for i in range(len(y_test)):\n",
    "    nb_difference_plot.loc[i] = y_test.values[i] - nb_y_pred[i]\n",
    "                                                \n",
    "nb_difference_plot                                             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_difference_plot.plot(title = 'Naive Bayes test VS predict difference', figsize=(15,8))\n",
    "plt.xlabel('ID')\n",
    "plt.ylabel('Car Price Difference')\n",
    "plt.grid(True)\n",
    "plt.grid(alpha = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3020.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>211.33245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14110.09777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-82095.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-2300.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2660.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>289020.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               diff\n",
       "count    3020.00000\n",
       "mean      211.33245\n",
       "std     14110.09777\n",
       "min    -82095.00000\n",
       "25%     -2300.00000\n",
       "50%         0.00000\n",
       "75%      2660.50000\n",
       "max    289020.00000"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_difference_plot['diff'] =nb_difference_plot['diff'].astype('int')\n",
    "nb_difference_plot.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean is about 211, it's the value really close to 0. Looks the predict only have small range of error.\n",
    "\n",
    "However, it's maybe due to the positive and negative value are balance each other. So, we make every value positive and see what the result is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3020.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5598.530464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12953.208477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2495.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>289020.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                diff\n",
       "count    3020.000000\n",
       "mean     5598.530464\n",
       "std     12953.208477\n",
       "min         0.000000\n",
       "25%      1000.000000\n",
       "50%      2495.000000\n",
       "75%      5400.000000\n",
       "max    289020.000000"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_abs_difference_plot = nb_difference_plot.copy()\n",
    "for i in range(len(nb_abs_difference_plot)):\n",
    "    if(nb_abs_difference_plot.loc[i].values < 0):\n",
    "        nb_abs_difference_plot.loc[i] *= -1;\n",
    "        \n",
    "nb_abs_difference_plot.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_abs_difference_plot.plot(title = 'Naive Bayes test VS predict absolute difference', figsize=(15,8))\n",
    "plt.xlabel('ID')\n",
    "plt.ylabel('Car Price Difference')\n",
    "plt.grid(True)\n",
    "plt.grid(alpha = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.23% datas difference small than 500\n",
      "21.89% datas difference small than 1000\n",
      "73.21% datas difference small than 5000\n"
     ]
    }
   ],
   "source": [
    "less_500 = 0\n",
    "less_1000 = 0\n",
    "less_5000 = 0\n",
    "\n",
    "for i in range(len(nb_abs_difference_plot)):\n",
    "    if(nb_abs_difference_plot.loc[i].values < 5000):\n",
    "        less_5000 += 1;\n",
    "    else:\n",
    "        continue\n",
    "        \n",
    "    if(nb_abs_difference_plot.loc[i].values < 1000):\n",
    "        less_1000 += 1;\n",
    "    else:\n",
    "        continue\n",
    "        \n",
    "    if(nb_abs_difference_plot.loc[i].values < 500):\n",
    "        less_500 += 1;\n",
    "    \n",
    "print(\"{:.2f}\".format(less_500 / len(nb_abs_difference_plot) * 100) + \"% datas difference small than 500\")\n",
    "print(\"{:.2f}\".format(less_1000 / len(nb_abs_difference_plot) * 100) + \"% datas difference small than 1000\")\n",
    "print(\"{:.2f}\".format(less_5000 / len(nb_abs_difference_plot) * 100) + \"% datas difference small than 5000\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naive Bayes is not that accurate with this data set."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
